Research on intelligent search-and-secure technology in accelerator hazardous areas based on machine vision

Prompt radiation emitted during accelerator operation poses a significant health risk,necessitating a thorough search and securing of hazardous areas prior to initiation.Currently,manual sweep methods are employed.However,the limitations of manual sweeps have become increasingly evident with the implementation of large-scale accelerators.By leveraging advancements in machine vision technology,the automatic identification of stranded personnel in controlled areas through camera imagery presents a viable solution for efficient search and security.Given the criticality of personal safety for stranded individuals,search and security processes must be sufficiently reliable.To ensure comprehensive coverage,180°camera groups were strategically positioned on both sides of the accelerator tunnel to eliminate blind spots within the monitoring range.The YOLOV8 network model was modified to enable the detection of small targets,such as hands and feet,as well as larger targets formed by individuals near the cameras.Furthermore,the system incorporates a pedestrian recognition model that detects human body parts,and an information fusion strategy is used to integrate the detected head,hands,and feet with the identified pedestrians as a cohesive unit.This strategy enhanced the capability of the model to identify pedestrians obstructed by equipment,resulting in a notable improvement in the recall rate.Specifically,recall rates of 0.915 and 0.82were obtained for Datasets 1 and 2,respectively.Although there was a slight decrease in accuracy,it aligned with the intended purpose of the search-and-secure software design.Experimental tests conducted within an accelerator tunnel demonstrated the effectiveness of this approach in achieving reliable recognition outcomes.

Multiple Measurement Models of Articulated Arm Coordinate Measuring Machines

The existing articulated arm coordinate measuring machines（AACMM） with one measurement model are easy to cause low measurement accuracy because the whole sampling space is much bigger than the result in the unstable calibration parameters. To compensate for the deficiency of one measurement model, the multiple measurement models are built by the Denavit-Hartenberg＇s notation, the homemade standard rod components are used as a calibration tool and the Levenberg-Marquardt calibration algorithm is applied to solve the structural parameters in the measurement models. During the tests of multiple measurement models, the sample areas are selected in two situations. It is found that the measurement errors＇ sigma value（0.083 4 ram） dealt with one measurement model is nearly two times larger than that of the multiple measurement models（0.043 1 ram） in the same sample area. While in the different sample area, the measurement errors＇ sigma value（0.054 0 ram） dealt with the multiple measurement models is about 40% of one measurement model（0.137 3 mm）. The preliminary results suggest that the measurement accuracy of AACMM dealt with multiple measurement models is superior to the accuracy of the existing machine with one measurement model. This paper proposes the multiple measurement models to improve the measurement accuracy of AACMM without increasing any hardware cost.

Identification of the Quaternary low gas-saturation reservoirs in the Sanhu area of the Qaidam Basin,China

Low gas-saturation reservoirs are gas bearing intervals whose gas saturation is less than 47%. They are common in the Quaternary of the Sanhu area in the Qaidam Basin.Due to the complex genesis mechanisms and special geological characteristics,the logging curves of low gas-saturation reservoirs are characterized by ambiguity and diversity,namely without significant log response characteristics. Therefore,it is particularly difficult to identify the low gas-saturation reservoirs in the study area.In addition,the traditional methods such as using the relations among lithology,electrical property,physical property and gas bearing property,as well as their threshold values,can not effectively identify low gas-saturation reservoirs.To solve this problem,we adopt the decision tree,support vector machine and rough set methods to establish a predictive model of low gas-saturation reservoirs,which is capable of classifying a mass of multi-dimensional and fuzzy data.According to the transparency of learning processes and the understandability of learning results,the predictive model was also revised by absorbing the actual reservoir characteristics.Practical applications indicate that the predictive model is effective in identifying low gas-saturation reservoirs in the study area.

Eco-Friendly Selection of Diesel Generator Based on Genset-Synchro Technology for Off-Grid Remote Area Application in the North of Quebec

For most of their energy requirements, greater part of remote communities and small islands around the world rely on imported fossil fuels. The economical cost of energy is therefore very high not only due to inherent cost of fuel, but also due to transportation and due to maintenance costs. One solution for saving fuel in a diesel generator is to allow the engine to operate directly in relation to the request for electrical load at variable speeds. Genset-Synchro Technology has developed an innovative variable speed?generator technology (patent pending) that allows applications where power demand varies widely to benefit from the new technology that maintains constant voltage and frequency while adjusting the generator stator speed to power demand. This paper will present an innovative approach for optimizing the energy production based from the fact that the structure that contains the stator windings of the generator is mounted on roller bearings, which allows its free rotation around the axis of the rotor, consequently stopping the stator structure from being static and aims to minimize the unit cost of electricity. Case study on application in remote area in the north of Quebec is described. A saving of 7%?-?9% on fuel consumption and greenhouse gas (GHG) under low winter ambient temperatures has been registered.

Comparison of LR,5-CV SVM,GA SVM,and PSO SVM for landslide susceptibility assessment in Tibetan Plateau area,China

The applicability of statistics-based landslide susceptibility assessment methods is affected by the number of historical landslides.Previous studies have proposed support vector machine(SVM)as a small-sample learning method.However,those studies demonstrated that different parameters can affect model performance.We optimized the SVM and obtained models as 5-fold cross validation(5-CV)SVM,genetic algorithm(GA)SVM,and particle swarm optimization(PSO)SVM.This study compared the prediction performances of logistic regression(LR),5-CV SVM,GA SVM,and PSO SVM on landslide susceptibility mapping,to explore the spatial distribution of landslide susceptibility in the study area in Tibetan Plateau,China.A geospatial database was established based on 392 historical landslides and 392 non-landslides in the study area.We used 11 influencing factors of altitude,slope,aspect,curvature,lithology,normalized difference vegetation index(NDVI),distance to road,distance to river,distance to fault,peak ground acceleration(PGA),and rainfall to construct an influencing factor evaluation system.To evaluate the models,four susceptibility maps were compared via receiver operating characteristics(ROC)curve and the results showed that prediction rates for the models are 84%(LR),87%(5-CV SVM),85%(GA SVM),and 90%(PSO SVM).We also used precision,recall,F1-score and accuracy to assess the quality performance of these models.The results showed that the PSO SVM had greater potential for future implementation in the Tibetan Plateau area because of its superior performance in the landslide susceptibility assessment.

POWER OPTIMIZATION OF FINITE STATE MACHINE BASED ON GENETIC ALGORITHM

Using state assignment to minimize power dissipation and area for finite state ma-chines is computationally hard. Most of published results show that the reduction of switchingactivity often trades with area penalty. In this paper, a new approach is proposed. Experimentalresults show a significant reduction of switching activity without area penalty compared withprevious publications.

Landslide susceptibility assessment in Western Henan Province based on a comparison of conventional and ensemble machine learning

Landslide is a serious natural disaster next only to earthquake and flood,which will cause a great threat to people’s lives and property safety.The traditional research of landslide disaster based on experience-driven or statistical model and its assessment results are subjective,difficult to quantify,and no pertinence.As a new research method for landslide susceptibility assessment,machine learning can greatly improve the landslide susceptibility model’s accuracy by constructing statistical models.Taking Western Henan for example,the study selected 16 landslide influencing factors such as topography,geological environment,hydrological conditions,and human activities,and 11 landslide factors with the most significant influence on the landslide were selected by the recursive feature elimination(RFE)method.Five machine learning methods[Support Vector Machines(SVM),Logistic Regression(LR),Random Forest(RF),Extreme Gradient Boosting(XGBoost),and Linear Discriminant Analysis(LDA)]were used to construct the spatial distribution model of landslide susceptibility.The models were evaluated by the receiver operating characteristic curve and statistical index.After analysis and comparison,the XGBoost model(AUC 0.8759)performed the best and was suitable for dealing with regression problems.The model had a high adaptability to landslide data.According to the landslide susceptibility map of the five models,the overall distribution can be observed.The extremely high and high susceptibility areas are distributed in the Funiu Mountain range in the southwest,the Xiaoshan Mountain range in the west,and the Yellow River Basin in the north.These areas have large terrain fluctuations,complicated geological structural environments and frequent human engineering activities.The extremely high and highly prone areas were 12043.3 km^(2)and 3087.45 km^(2),accounting for 47.61%and 12.20%of the total area of the study area,respectively.Our study reflects the distribution of landslide susceptibility in western Henan Province,which provides a scientific basis for regional disaster warning,prediction,and resource protection.The study has important practical significance for subsequent landslide disaster management.

Generation of tool path for flank milling in divided areas

Presents the division of non developable ruled surface into divided small areas and flank milling in these divided areas to improve machining efficiency and machined surface quality by controlling the machining error for each area, and the algorithms developed for generation of tool path and calculation of errors, and concludes from computer simulation results that the algorithms are correct.

Stand basal area modelling for Chinese fir plantations using an artificial neural network model

Artificial neural network models are a popular estimation tool for fitting nonlinear relationships because they require no assumptions about the form of the fitting function,non-Gaussian distributions,multicollinearity,outliers and noise in the data.The problems of backpropagation models using artificial neural networks include determination of the structure of the network and overlearning courses.According to data from 1981 to 2008 from 15 permanent sample plots on Dagangshan Mountain in Jiangxi Province,a back-propagation artificial neural network model(BPANN)and a support vector machine model(SVM)for basal area of Chinese fir(Cunninghamia lanceolata)plantations were constructed using four kinds of prediction factors,including stand age,site index,surviving stem numbers and quadratic mean diameters.Artificial intelligence methods,especially SVM,could be effective in describing stand basal area growth of Chinese fir under different growth conditions with higher simulation precision than traditional regression models.SVM and the Chapman–Richards nonlinear mixed-effects model had less systematic bias than the BPANN.

Adaptive Data Transmission Method According to Wireless State in Long Range Wide Area Networks

The Internet of Things(IoT)has enabled various intelligent services,and IoT service range has been steadily extended through long range wide area communication technologies,which enable very long distance wireless data transmission.End-nodes are connected to a gateway with a single hop.They consume very low-power,using very low data rate to deliver data.Since long transmission time is consequently needed for each data packet transmission in long range wide area networks,data transmission should be efficiently performed.Therefore,this paper proposes a multicast uplink data transmission mechanism particularly for bad network conditions.Transmission delay will be increased if only retransmissions are used under bad network conditions.However,employing multicast techniques in bad network conditions can significantly increase packet delivery rate.Thus,retransmission can be reduced and hence transmission efficiency increased.Therefore,the proposed method adopts multicast uplink after network condition prediction.To predict network conditions,the proposed method uses a deep neural network algorithm.The proposed method performance was verified by comparison with uplink unicast transmission only,confirming significantly improved performance.

Measurement of the Effective Dose Radiation at Radiology Departments of Some Hospitals in Duhok Governorate

During operating of the X-ray machines, if the protection of X-ray rooms is insufficient, not only the patient but also clinical staffs as well as public are exposed to high X-ray dosage and they are affected from X-ray related to the dose level. In the present survey, by testing the radiological leakage and scatter from X-rays machines in radiology departments of 7 randomly selected hospitals in Duhok governorate, the effects dose of X-ray to the both control panel area and the patients waiting or visiting area who are located near the radiography room, were measured. The dose was recorded for a range of peak kilovoltage (kVp) and mAs values to find efficiency of shielding materials (barriers) of radiography rooms for different X-rays level. The measurements were performed at one meter above the ground surface which was the same height of X-rays tube by using Gamma Scout dosimeter. From the measurement results, it was seen that the most hospitals barriers (doors and walls) were not appropriate to the standards except 2 hospitals. The maximum effective doses were measured in uncontrolled area of Khazer hospital which was 82.48 ± 0.73 mSv·yr-1 that was much more than the reference dose limits and in controlled area of Haval Banda Zaroka hospital which was 12.98 ± 0.16 mSv·yr-1. In result, the knowledge about the radiation dose affecting the radiologists and public in the selected hospitals was obtained, and by informing the radiologists and the hospitals managements, the necessary regulations would be planned.

现场总线PROFIBUS及其应用

介绍了现场总线PROFIBUS的概念、组成,现场总线PROFIBUS中PROFIBUS-DP协议、特点、网络配置及其在攀钢集团成都钢铁有限责任公司Φ340mm连轧管机组矫直机区域辅机系统的基础自动化控制系统中的应用;基础自动化控制系统硬件和软件的选择,系统组成及其功能等。

Machine Learning-Based Threatened Species Translocation Under Climate Vulnerability

Climate change is the most serious causes and has a direct impact on biodiversity.According to the world’s biodiversity conservation organization,rep-tile species are most affected since their biological and ecological qualities are directly linked to climate.Due to a lack of time frame in existing works,conser-vation adoption affects the performance of existing works.The proposed research presents a knowledge-driven Decision Support System(DSS)including the assisted translocation to adapt to future climate change to conserving from its extinction.The Dynamic approach is used to develop a knowledge-driven DSS using machine learning by applying an ecological and biological variable that characterizes the model and mitigation processes for species.However,the frame-work demonstrates the huge difference in the estimated significance of climate change,the model strategy helps to recognize the probable risk of threatened spe-cies translocation to future climate change.The proposed system is evaluated using various performance metrics and this framework can comfortably adapt to the decisions support to reintroduce the species for conservation in the future.

A Comprehensive Analysis of Datasets for Automotive Intrusion Detection Systems

Recently,automotive intrusion detection systems(IDSs)have emerged as promising defense approaches to counter attacks on in-vehicle networks(IVNs).However,the effectiveness of IDSs relies heavily on the quality of the datasets used for training and evaluation.Despite the availability of several datasets for automotive IDSs,there has been a lack of comprehensive analysis focusing on assessing these datasets.This paper aims to address the need for dataset assessment in the context of automotive IDSs.It proposes qualitative and quantitative metrics that are independent of specific automotive IDSs,to evaluate the quality of datasets.These metrics take into consideration various aspects such as dataset description,collection environment,and attack complexity.This paper evaluates eight commonly used datasets for automotive IDSs using the proposed metrics.The evaluation reveals biases in the datasets,particularly in terms of limited contexts and lack of diversity.Additionally,it highlights that the attacks in the datasets were mostly injected without considering normal behaviors,which poses challenges for training and evaluating machine learning-based IDSs.This paper emphasizes the importance of addressing the identified limitations in existing datasets to improve the performance and adaptability of automotive IDSs.The proposed metrics can serve as valuable guidelines for researchers and practitioners in selecting and constructing high-quality datasets for automotive security applications.Finally,this paper presents the requirements for high-quality datasets,including the need for representativeness,diversity,and balance.

Analysis of ink flow channel between two rotating ink rollers

In order to research how the size of the ink flow channel is affected by the interaction of adjacent ink areas,according to the method of fluid-solid interaction,this paper analyzes the size of the ink flow channel of two ink rollers' rotation.Firstly,this paper simulates the situation of only one ink area.Secondly,this paper simulates the situation of five adjacent ink areas.Then,through comparing the simulation results of two above situations,it' s obvious that the interaction of adjacent ink areas has big effects on the ink pressure and the size of ink flow channel.At last,this paper gets the main factor that affects the size of ink flow channel in the different situations.

Object-Based Burned Area Mapping with Extreme Gradient Boosting Using Sentinel-2 Imagery

The Sentinel-2 satellites are providing an unparalleled wealth of high-resolution remotely sensed information with a short revisit cycle, which is ideal for mapping burned areas both accurately and timely. This paper proposes an automated methodology for mapping burn scars using pairs of Sentinel-2 imagery, exploiting the state-of-the-art eXtreme Gradient Boosting (XGB) machine learning framework. A large database of 64 reference wildfire perimeters in Greece from 2016 to 2019 is used to train the classifier. An empirical methodology for appropriately sampling the training patterns from this database is formulated, which guarantees the effectiveness of the approach and its computational efficiency. A difference (pre-fire minus post-fire) spectral index is used for this purpose, upon which we appropriately identify the clear and fuzzy value ranges. To reduce the data volume, a super-pixel segmentation of the images is also employed, implemented via the QuickShift algorithm. The cross-validation results showcase the effectiveness of the proposed algorithm, with the average commission and omission errors being 9% and 2%, respectively, and the average Matthews correlation coefficient (MCC) equal to 0.93.

Effect of Repainting Process on Atmospheric Corrosion Behavior of Perforated Panel

Steel structures are often painted to protect against corrosion. Repainting is one of the most important factors in maintaining the corrosion protection function of the coating. Various factors affect the life of the coating film, such as the surface preparation, the type of coating, and the number of coats. Surface preparation is important for the life of the coating film. However, appropriate surface treatment is difficult due to the complex shape of perforated panels, and it has been confirmed that corrosion progresses from the machined area. Therefore, appropriate pretreatment of the machined area is important for corrosion prevention. In this study, we investigated the effects of the repainting process on the atmospheric corrosion behavior of perforated panels. To reproduce the repainting process, a number of pretreatments were performed, such as salt spraying, blasting, and zinc phosphate treatment. In the salt spray test after pretreatment and painting, the corrosion progressed in cases with no zinc phosphate treatment and those left untreated for 48 h before painting. In addition, the coating film on the processed area was confirmed to be thin. These results suggested that appropriate pretreatment and sufficient thickness of the coating on the machined area would affect the occurrence of corrosion.

A Human Face Recognition Method based on the Core Characteristics under the Condition of Illumination

A core characteristics based human face recognition method under the condition of illumination is proposed according to the problem of the sharply declining human face recognition rate under the condition of lighL With this method, if human face image is affected by light and the illumination is forward or side can be judged; the images affect by illumination are processed using the strategy of frequency domain replacement, and then the key areas of human face image are divided and then are recognized using support vector machine （SVM） based on the unit of area, and finally the recognition results are integrated. The experimental result shows that this method can produce a better recognition effect than other methods in view of the problem of illumination.

淮北矿区多种类型植被地上生物量反演研究

生物量是植被碳的重要组成部分,对于评估矿山生态环境修复效果至关重要。在淮北矿业闸河矿区,生态破坏与修复并存,地表植被类型丰富多样,有林木、草本和农作物等。在这种多植被类型交叉覆盖的地区,传统的单一物种生物量模型难以应用。为准确反演该区域综合植被的生物量,以sentinel卫星数据为基础,利用植被指数和波段运算方法构建的光谱指数作为特征数据集,通过不同特征和模型的组合构建该区域生物量反演模型。在特征选择阶段,利用随机森林和相关性分析两种方法对特征进行综合评价,最终筛选出关键的5个光谱指数(增强植被指数、归一化差分水体指数、改进的归一化差分水体指数、Sentinel-2红边位置、陆地叶绿素指数)以及5个基于波段运算的自建光谱特征(1/B1、1/B2、1/B7、B1/B2、B5/B6)。基于这些筛选出的特征,设计了5种不同的特征组合方案,并结合传统方程回归和机器学习模型,在林地和综合植被数据上分别构建了生物量反演模型。研究结果表明,相较于传统回归模型,机器学习模型在生物量反演领域具有更高的精度。基于波段运算的自建光谱特征可以提高机器学习模型生物量反演的精度,其中支持向量回归(SVR)模型结合自建光谱特征与原始波段得到的反演结果精度最高,模型验证集的决定系数R^(2)为0.74,均方根误差为8.14kg·m^(-2)。将SVR模型应用于研究区进行生物量反演,得到的结果与当地植被分布特征高度一致。本研究成果不仅为矿区生态修复评价提供了数据支撑,而且为类似生态系统的后续研究提供了借鉴和经验。