A Simple and Effective Surface Defect Detection Method of Power Line Insulators for Difficult Small Objects

Insulator defect detection plays a vital role in maintaining the secure operation of power systems.To address the issues of the difficulty of detecting small objects and missing objects due to the small scale,variable scale,and fuzzy edge morphology of insulator defects,we construct an insulator dataset with 1600 samples containing flashovers and breakages.Then a simple and effective surface defect detection method of power line insulators for difficult small objects is proposed.Firstly,a high-resolution featuremap is introduced and a small object prediction layer is added so that the model can detect tiny objects.Secondly,a simplified adaptive spatial feature fusion(SASFF)module is introduced to perform cross-scale spatial fusion to improve adaptability to variable multi-scale features.Finally,we propose an enhanced deformable attention mechanism(EDAM)module.By integrating a gating activation function,the model is further inspired to learn a small number of critical sampling points near reference points.And the module can improve the perception of object morphology.The experimental results indicate that concerning the dataset of flashover and breakage defects,this method improves the performance of YOLOv5,YOLOv7,and YOLOv8.In practical application,it can simply and effectively improve the precision of power line insulator defect detection and reduce missing detection for difficult small objects.

Effect of low-degree astigmatism on the objective visual quality

AIM:To evaluate the effect of low-degree astigmatism on objective visual quality through the Optical Quality Analysis System(OQAS).METHODS:This study enrolled 46 participants(aged 23 to 30y,90 eyes)with normal or corrected-to-normal vision.The cylindrical lenses(0,0.5,0.75,1.0,and 1.25 D)were placed at the axial direction(180°,45°,90°,and 135°)in front of the eyes with the best correction to form 16 types of regular low-degree astigmatism.OQAS was used to detect the objective visual quality,recorded as the objective scattering index(OSI),OQAS values at contrasts of 100%,20%,and 9%predictive visual acuity(OV100%,OV20%,and OV9%),modulation transfer function cut-off(MTFcut-off)and Strehl ratio(SR).The mixed effect linear model was used to compare objective visual quality differences between groups and examine associations between astigmatic magnitude and objective visual quality parameters.RESULTS:Apparent negative relationships between the magnitude of low astigmatism and objective visual quality were observed.The increase of OSI per degree of astigmatism at 180°,45°,90°,and 135°axis were 0.38(95%CI:0.35,0.42),0.50(95%CI:0.46,0.53),0.49(95%CI:0.45,0.54)and 0.37(95%CI:0.34,0.41),respectively.The decrease of MTFcut-off per degree of astigmatism at 180°,45°,90°,and 135°axis were-10.30(95%CI:-11.43,-9.16),-12.73(95%CI:-13.62,-11.86),-12.75(95%CI:-13.79,-11.70),and-9.97(95%CI:-10.92,-9.03),respectively.At the same astigmatism degree,OSI at 45°and 90°axis were higher than that at 0°and 135°axis,while MTFcut-off were lower.CONCLUSION:Low astigmatism of only 0.50 D can significantly reduce the objective visual quality.

Promotion of structural plasticity in area V2 of visual cortex prevents against object recognition memory deficits in aging and Alzheimer's disease rodents

Memory deficit,which is often associated with aging and many psychiatric,neurological,and neurodegenerative diseases,has been a challenging issue for treatment.Up till now,all potential drug candidates have failed to produce satisfa ctory effects.Therefore,in the search for a solution,we found that a treatment with the gene corresponding to the RGS14414protein in visual area V2,a brain area connected with brain circuits of the ventral stream and the medial temporal lobe,which is crucial for object recognition memory(ORM),can induce enhancement of ORM.In this study,we demonstrated that the same treatment with RGS14414in visual area V2,which is relatively unaffected in neurodegenerative diseases such as Alzheimer s disease,produced longlasting enhancement of ORM in young animals and prevent ORM deficits in rodent models of aging and Alzheimer’s disease.Furthermore,we found that the prevention of memory deficits was mediated through the upregulation of neuronal arbo rization and spine density,as well as an increase in brain-derived neurotrophic factor(BDNF).A knockdown of BDNF gene in RGS14414-treated aging rats and Alzheimer s disease model mice caused complete loss in the upregulation of neuronal structural plasticity and in the prevention of ORM deficits.These findings suggest that BDNF-mediated neuronal structural plasticity in area V2 is crucial in the prevention of memory deficits in RGS14414-treated rodent models of aging and Alzheimer’s disease.Therefore,our findings of RGS14414gene-mediated activation of neuronal circuits in visual area V2 have therapeutic relevance in the treatment of memory deficits.

Development and optimization of object detection technology in pavement engineering: A literature review

Due to the rapid advancement of the transportation industry and the continual increase in pavement infrastructure,it is difficult to keep up with the huge road maintenance task by relying only on the traditional manual detection method.Intelligent pavement detection technology with deep learning techniques is available for the research and industry areas by the gradual development of computer vision technology.Due to the different characteristics of pavement distress and the uncertainty of the external environment,this kind of object detection technology for distress classification and location still faces great challenges.This paper discusses the development of object detection technology and analyzes classical convolutional neural network(CNN)architecture.In addition to the one-stage and two-stage object detection frameworks,object detection without anchor frames is introduced,which is divided according to whether the anchor box is used or not.This paper also introduces attention mechanisms based on convolutional neural networks and emphasizes the performance of these mechanisms to further enhance the accuracy of object recognition.Lightweight network architecture is introduced for mobile and industrial deployment.Since stereo cameras and sensors are rapidly developed,a detailed summary of three-dimensional object detection algorithms is also provided.While reviewing the history of the development of object detection,the scope of this review is not only limited to the area of pavement crack detection but also guidance for researchers in related fields is shared.

Management of Penetrating Cranioencephalic Trauma Caused by Sharp Metal Objects—Therapeutic and Evolutionary Aspects: 12 Cases at the Renaissance University Hospital in N’Djamena

Introduction: Cranioencephalic trauma caused by bladed weapons is rare, and that caused by sharp objects is exceptional. The aim of our study was to describe the clinical, therapeutic and evolutionary aspects. Materials and method: This was a descriptive and analytical study over a 48-month period at CHU la Renaissance from January 1, 2018 to December 31, 2021, concerning patients admitted for penetrating cranioencephalic trauma by pointed object. Results: Twelve cases, all male, of penetrating cranioencephalic sharp-force trauma were identified. The mean age was 34 ± 7 years, with extremes of 11 and 60 years. Farmers and herders accounted for 31% and 25% of cases respectively. The average admission time was 47 hours. Brawls were the circumstances of occurrence in 81.2% of cases. Knives (33%), arrows (25%) and iron bars (16.6%) were the objects used. Altered consciousness was present in 43.8% of cases, and focal deficit in 50%. Scannographic lesions were fracture and/or embarrhment (12 cases), intra-parenchymal haematomas (6 cases) and presence of object in place (4 cases). Surgery was performed in 11 patients. Postoperative outcome was favorable in 9 patients. After 12 months, 2 patients were declared unfit. Conclusion: Penetrating head injuries caused by sharp objects are common in Chad. Urgent surgery can prevent disabling after-effects.

Bioinspired Passive Tactile Sensors Enabled by Reversible Polarization of Conjugated Polymers

Tactile perception plays a vital role for the human body and is also highly desired for smart prosthesis and advanced robots.Compared to active sensing devices,passive piezoelectric and triboelectric tactile sensors consume less power,but lack the capability to resolve static stimuli.Here,we address this issue by utilizing the unique polarization chemistry of conjugated polymers for the first time and propose a new type of bioinspired,passive,and bio-friendly tactile sensors for resolving both static and dynamic stimuli.Specifically,to emulate the polarization process of natural sensory cells,conjugated polymers(including poly(3,4-ethylenedioxythiophen e):poly(styrenesulfonate),polyaniline,or polypyrrole)are controllably polarized into two opposite states to create artificial potential differences.The controllable and reversible polarization process of the conjugated polymers is fully in situ characterized.Then,a micro-structured ionic electrolyte is employed to imitate the natural ion channels and to encode external touch stimulations into the variation in potential difference outputs.Compared with the currently existing tactile sensing devices,the developed tactile sensors feature distinct characteristics including fully organic composition,high sensitivity(up to 773 mV N^(−1)),ultralow power consumption(nW),as well as superior bio-friendliness.As demonstrations,both single point tactile perception(surface texture perception and material property perception)and two-dimensional tactile recognitions(shape or profile perception)with high accuracy are successfully realized using self-defined machine learning algorithms.This tactile sensing concept innovation based on the polarization chemistry of conjugated polymers opens up a new path to create robotic tactile sensors and prosthetic electronic skins.

The Fusion of Temporal Sequence with Scene Priori Information in Deep Learning Object Recognition

For some important object recognition applications such as intelligent robots and unmanned driving, images are collected on a consecutive basis and associated among themselves, besides, the scenes have steady prior features. Yet existing technologies do not take full advantage of this information. In order to take object recognition further than existing algorithms in the above application, an object recognition method that fuses temporal sequence with scene priori information is proposed. This method first employs YOLOv3 as the basic algorithm to recognize objects in single-frame images, then the DeepSort algorithm to establish association among potential objects recognized in images of different moments, and finally the confidence fusion method and temporal boundary processing method designed herein to fuse, at the decision level, temporal sequence information with scene priori information. Experiments using public datasets and self-built industrial scene datasets show that due to the expansion of information sources, the quality of single-frame images has less impact on the recognition results, whereby the object recognition is greatly improved. It is presented herein as a widely applicable framework for the fusion of information under multiple classes. All the object recognition algorithms that output object class, location information and recognition confidence at the same time can be integrated into this information fusion framework to improve performance.

Patterns of Interactions of the Complex City System:Emotional Urban Objects as Triggering Agents-A Secondary Publication

This article presents an analysis of the patterns of interactions resulting from the positive and negative emotional events that occur in cities,considering them as complex systems.It explores,from the imaginaries,how certain urban objects can act as emotional agents and how these events affect the urban system as a whole.An adaptive complex systems perspective is used to analyze these patterns.The results show patterns in the processes and dynamics that occur in cities based on the objects that affect the emotions of the people who live there.These patterns depend on the characteristics of the emotional charge of urban objects,but they can be generalized in the following process:(1)immediate reaction by some individuals;(2)emotions are generated at the individual level which begins to generalize,permuting to a collective emotion;(3)a process of reflection is detonated in some individuals from the reading of collective emotions;(4)integration/significance in the community both at the individual and collective level,on the concepts,roles and/or functions that give rise to the process in the system.Therefore,it is clear that emotions play a significant role in the development of cities and these aspects should be considered in the design strategies of all kinds of projects for the city.Future extensions of this work could include a deeper analysis of specific emotional events in urban environments,as well as possible implications for urban policy and decision making.

Advancing Crowd Object Detection: A Review of YOLO, CNN and ViTs Hybrid Approach*

One of the most basic and difficult areas of computer vision and image understanding applications is still object detection. Deep neural network models and enhanced object representation have led to significant progress in object detection. This research investigates in greater detail how object detection has changed in the recent years in the deep learning age. We provide an overview of the literature on a range of cutting-edge object identification algorithms and the theoretical underpinnings of these techniques. Deep learning technologies are contributing to substantial innovations in the field of object detection. While Convolutional Neural Networks (CNN) have laid a solid foundation, new models such as You Only Look Once (YOLO) and Vision Transformers (ViTs) have expanded the possibilities even further by providing high accuracy and fast detection in a variety of settings. Even with these developments, integrating CNN, YOLO and ViTs, into a coherent framework still poses challenges with juggling computing demand, speed, and accuracy especially in dynamic contexts. Real-time processing in applications like surveillance and autonomous driving necessitates improvements that take use of each model type’s advantages. The goal of this work is to provide an object detection system that maximizes detection speed and accuracy while decreasing processing requirements by integrating YOLO, CNN, and ViTs. Improving real-time detection performance in changing weather and light exposure circumstances, as well as detecting small or partially obscured objects in crowded cities, are among the goals. We provide a hybrid architecture which leverages CNN for robust feature extraction, YOLO for rapid detection, and ViTs for remarkable global context capture via self-attention techniques. Using an innovative training regimen that prioritizes flexible learning rates and data augmentation procedures, the model is trained on an extensive dataset of urban settings. Compared to solo YOLO, CNN, or ViTs models, the suggested model exhibits an increase in detection accuracy. This improvement is especially noticeable in difficult situations such settings with high occlusion and low light. In addition, it attains a decrease in inference time in comparison to baseline models, allowing real-time object detection without performance loss. This work introduces a novel method of object identification that integrates CNN, YOLO and ViTs, in a synergistic way. The resultant framework extends the use of integrated deep learning models in practical applications while also setting a new standard for detection performance under a variety of conditions. Our research advances computer vision by providing a scalable and effective approach to object identification problems. Its possible uses include autonomous navigation, security, and other areas.

Research on the Relationship between the School-Running Characteristics of Private Colleges and Universities and the Development Objectives of the Government

The development objectives of the Chinese government have a guiding effect on the school-running characteristics of private colleges and universities.This paper explores the relationship between the characteristics of private colleges and universities and the development objectives of the provincial government,using Jiangsu Province as a case study.All the schools have the consciousness of fully considering the development objectives of Jiangsu Province when formulating the school-running characteristics.Their characteristics all contain some training methods,training courses,or institutional construction aiming at the development objectives.Most universities can support the government’s objectives on employment and innovation.Only a third of the schools provide government support for science research.Private colleges and universities should make full use of the free and flexible advantages of private education,combine them with the continuous changes of policies and markets,and create clear,scientific,and dynamic school-running characteristics,in order to achieve long-term development.

MOVING OBJECT TRACKING IN DYNAMIC IMAGE SEQUENCE BASED ON ESTIMATION OF MOTION VECTORS OF FEATURE POINTS

An improved estimation of motion vectors of feature points is proposed for tracking moving objects of dynamic image sequence. Feature points are firstly extracted by the improved minimum intensity change （MIC） algorithm. The matching points of these feature points are then determined by adaptive rood pattern searching. Based on the random sample consensus （RANSAC） method, the background motion is finally compensated by the parameters of an affine transform of the background motion. With reasonable morphological filtering, the moving objects are completely extracted from the background, and then tracked accurately. Experimental results show that the improved method is successful on the motion background compensation and offers great promise in tracking moving objects of the dynamic image sequence.

Method of Establishing Object-Oriented System Structure for Decision Support System

In order to solve existing problems about the method of establishing traditional system structure of decision support system(DSS), O S chart is applied to describe object oriented system structure of general DSS, and a new method of eight specific steps is proposed to establish object oriented system structure of DSS by using the method of O S chart, which is applied successfully to the development of the DSS for the energy system ecology engineering research of the Wangheqiu country. Supplying many scientific effective computing models, decision support ways and a lot of accurate reliable decision data, the DSS plays a critical part in helping engineering researchers to make correct decisions. Because the period for developing the DSS is relatively shorter, the new way improves the efficiency of establishing DSS greatly. It also makes the DSS of system structure more flexible and easy to expand.

LOCALIZATION OF OBJECT (SPINE) IN MEDICAL IMAGE USING ACTIVE SHAPE MODELS

Active shape models (ASM), consisting of a shape model and a local gray-level appearance model, can be used to locate the objects in images. In original ASM scheme, the model of object′s gray-level variations is based on the assumption of one-dimensional sampling and searching method. In this work a new way to model the gray-level appearance of the objects is explored, using a two-dimensional sampling and searching technique in a rectangular area around each landmark of object shape. The ASM based on this improvement is compared with the original ASM on an identical medical image set for task of spine localization. Experiments demonstrate that the method produces significantly fast, effective, accurate results for spine localization in medical images.

Algorithm and System of Scanning Color 3D Objects

This paper presents a complete system for scanning the geometry and texture of a large 3D object, then the automatic registration is performed to obtain a whole realistic 3D model. This system is composed of one line strip laser and one color CCD camera. The scanned object is pictured twice by a color CCD camera. First, the texture of the scanned object is taken by a color CCD camera. Then the 3D information of the scanned object is obtained from laser plane equations. This paper presents a practical way to implement the three dimensional measuring method and the automatic registration of a large 3D object and a pretty good result is obtained after experiment verification.

Objectives of and Approaches to Ecological City Construction

It has become a trend of the city development in the world to build up the ecological city with an ecological system that is balanced in structure,high-efficient in service function.Taking Suzhou as a case,the authors believe that the construction of an ecological city is a process of the progressive and well-organized systematic development and of the ecological function consummation,which will require five stages to complete,including ecological hygiene,ecological security,ecological industry,ecological landscape,and ecological culture.The problems presently encountered in the construction of ecological city in Suzhou can be solved through the following approaches:(1) To rebuild the relationship between man and nature when the planning is worked out;(2) To observe the principle of ecology in city design;(3) To choose the local plant species in city afforestation;(4) To choose those plantlets with vigorous growth,good shape,and high survival rate,observing the growth rule of plant and paying much attention to forest landscape,ecological system and biological diversity,rationally allocating the plant species resources;(5) To aim at the implementation of the less-labor management even the zero-labor management.

Modeling of Intelligent Integrated Network Management System Using Object Oriented Approach *

The concept of intelligent integrated network management (IINM) is briefly introduced. In order to analyze, design and implement IINM successfully, object oriented approach is testified to be an effective and efficient way. In this paper, object oriented technique is applied to the structural model of IINM system, The Domain object class and the MU object class are used to represent the manager and the managed resources. Especially, NM IA is introduced which is a special object class with intelligent behaviors to manage the resources efficiently.

DESIGN OF WINDOW BASED AND OBJECT ORIENTED GUI

This paper discusses the design concept and method about window based and object oriented Graphic User Interface(GUI),and describes the definition of each class in detail. It is developed with Watcom C ++ in DOS environment.The GUI can be redeveloped conveniently and effectively by users.It consists of window,popup menu,icon,button and other components.

From Concrete Object ＂Tress＂ of English Poems to Abstract Image in Chinese Counterparts

Image is an important and creative way to express poets＂ feelings in both Chinese and English poetry. There are concrete representations and abstract concept in image. They are two key notions in Poetics and Aesthetics. This paper is to show the different versions of ＂tress＂ in poems and to explore the exact nature of concepts of sensitive affection in English and Chinese, so as to appreciate the artistic beauty of images.

Sediment distribution pattern mapped from the combination of objective analysis and geostatistics in the large shallow Taihu Lake, China

Investigation was made into sediment depth at 723 irregularly scattered measurement points which cover all the regions in Taihu Lake, China. The combination of successive correction scheme and geostatistical method was used to get all the values of recent sediment thickness at the 69×69 grids in the whole lake. The results showed that there is the significant difference in sediment depth between the eastern area and the western region, and most of the sediments are located in the western shore-line and northern regimes but just a little in the center and eastern parts. The notable exception is the patch between the center and Xishan Island where the maximum sediment depth is more than 4.0 m. This sediment distribution pattern is more than likely related to the current circulation pattern induced by the prevailing wind-forcing in Taihu Lake. The numerical simulation of hydrodynamics can strong support the conclusion. Sediment effects on water quality was also studied and the results showed that the concentrations of TP, TN and SS in the western part are obviously larger than those in the eastern regime, which suggested that more nutrients can be released from thicker sediment areas.

Canadian Agility and Movement Skill Assessment(CAMSA):Validity, objectivity, and reliability evidence for children 8–12 years of age

Purpose: The primary aim of this study was to develop an assessment of the fundamental, combined, and complex movement skills required to support childhood physical literacy. The secondary aim was to establish the feasibility, objectivity, and reliability evidence for the assessment.Methods: An expert advisory group recommended a course format for the assessment that would require children to complete a series of dynamic movement skills. Criterion-referenced skill performance and completion time were the recommended forms of evaluation. Children, 8–12 years of age, self-reported their age and gender and then completed the study assessments while attending local schools or day camps. Face validity was previously established through a Delphi expert(n = 19, 21% female) review process. Convergent validity was evaluated by age and gender associations with assessment performance. Inter-and intra-rater(n = 53, 34% female) objectivity and test–retest(n = 60, 47% female) reliability were assessed through repeated test administration.Results: Median total score was 21 of 28 points(range 5–28). Median completion time was 17 s. Total scores were feasible for all 995 children who self-reported age and gender. Total score did not differ between inside and outside environments(95% confidence interval(CI) of difference:-0.7 to 0.6;p = 0.91) or with/without footwear(95%CI of difference:-2.5 to 1.9; p = 0.77). Older age(p < 0.001, η2= 0.15) and male gender(p < 0.001, η2= 0.02)were associated with a higher total score. Inter-rater objectivity evidence was excellent(intraclass correlation coefficient(ICC) = 0.99) for completion time and substantial for skill score(ICC = 0.69) for 104 attempts by 53 children(34% female). Intra-rater objectivity was moderate(ICC = 0.52) for skill score and excellent for completion time(ICC = 0.99). Reliability was excellent for completion time over a short(2–4 days; ICC = 0.84) or long(8–14days; ICC = 0.82) interval. Skill score reliability was moderate(ICC = 0.46) over a short interval, and substantial(ICC = 0.74) over a long interval.Conclusion: The Canadian Agility and Movement Skill Assessment is a feasible measure of selected fundamental, complex and combined movement skills, which are an important building block for childhood physical literacy. Moderate-to-excellent objectivity was demonstrated for children 8–12 years of age. Test–retest reliability has been established over an interval of at least 1 week. The time and skill scores can be accurately estimated by 1 trained examiner.