Influence Of Human Factors Research Articles

A compound artificial nest designed for oviposition of various fish

Due to the influence of human factors and natural factors, the oviposition grounds and living environment of fish have been seriously damaged, and the decline of fishery resources is obvious. In recent years, artificial fish nests are widely served as a good means to restore fisheries resources and their degraded habitats, summarizes the materials, structure, nesting methods and existing problems for artificial fish nest, according to the artificial fish nest which is complicated to make, has a short service life and the existing artificial fish nest only aiming at the shortcomings of the herbaceous oocytes, we designed a compound artificial fish nest. The complex artificial fish nest consists of a frame, which is provided with two kinds of soft and hard egg adhesive medium to provide ecologic spawning conditions for grass spawning, gravel spawning and water spawning fish whose spawning ground is damaged, so as to increase and improve the population structure and quantity of wild fish.

Feature extraction method of face image texture spectrum based on a deep learning algorithm

Deep learning has made great progress in the field of face recognition, but most of the current face feature matching algorithms focus on the matching of a single image and another single image, and can not effectively use the relevant information between image sequences, in order to avoid the influence of human factors on the skin texture feature extraction of face image. In this paper, a texture spectrum feature extraction method based on deep learning is proposed. The face image is extracted by CNN network, and the similar image sequences are automatically selected for feature matching by using the improved sparse expression method to obtain the relevant information between the face image sequences. The experimental results show that the algorithm has achieved good results in LFW and AR databases and is superior to the traditional SRC, L1 norm and CRC-RLS algorithms.

Feature extraction method of face image texture spectrum based on a deep learning algorithm

Deep learning has made great progress in the field of face recognition, but most of the current face feature matching algorithms focus on the matching of a single image and another single image, and can not effectively use the relevant information between image sequences, in order to avoid the influence of human factors on the skin texture feature extraction of face image. In this paper, a texture spectrum feature extraction method based on deep learning is proposed. The face image is extracted by CNN network, and the similar image sequences are automatically selected for feature matching by using the improved sparse expression method to obtain the relevant information between the face image sequences. The experimental results show that the algorithm has achieved good results in LFW and AR databases and is superior to the traditional SRC, L1 norm and CRC-RLS algorithms.

Painting image quality on visual art analysis for information transmission of wireless networks

As a treasure of human civilisation, painting art has immeasurable artistic value. However, due to the influence of human factors, accidents and natural environment, there are different degrees of ...

Human factor in professional activity of aviation maintenance specialists as an element of flight security

Target of this article is to consider particularities of human factor influence in professional activity of aviation maintenance specialists. In consequence of theoretic analysis of studying of this problem were discovered preconditions and recent trends of development of human factor at specialists of this activity area as well as were identified structural components of mentioned phenomenon. According to that, the authors made a description of defining characteristics of manifestation of human factor in professional activity of aviation maintenance specialists: psychophysiological particularities of employees, professional requirements for carrying out of this activity and work conditions of those specialists. All this, at the opinion of authors, is one of determinants of human factor manifestation at aviation maintenance specialists. As to the improvement of professional activity level of specialists of this field, following levels of human factor manifestation at aviation maintenance specialists with precise hierarchic structure of its development were identified: sensory-perceptual level (work of human analyzer system), personal level (value-motivational aspect of carrying out of professional activity) and professional level (development of professional competence of personnel). At the basis thereof it was elaborated authors functional three-stage hierarchic model of human factor of aviation maintenance specialists. In order to decrease the risk of mistakes making by specialists during solving professional problems at any level of their committing, it was suggested a number of activities for improvement of human factor in professional activity of aviation maintenance specialists, which are directed to the development of sensory-perceptual, personal and professional levels.

Analysis and classification of tem¬perature measurements during melting and casting of alloys using neural networks

The article considers the issues of monitoring the thermal conditions of alloys melting and casting at foundries. It is noted that the least reliable method is when the measurement and fixing the temperature is assigned to the worker. On the other hand, a fully automatic approach is not always available for small foundries. In this regard, the expediency of using an automated approach is shown, in which the measurement is assigned to the worker, and the values are recorded automatically. This method assumes implementation of an algorithm for automatic classification of temperature measurements based on an end-to-end array of data obtained in the production stream. The solving of this task is divided into three stages. Preparing of raw data for classification process is provided on the first stage. On the second stage, the task of measurement classification is solved by using neural network principles. Analysis of the results of the artificial neural network has shown its high efficiency and degree of their correspondence with the actual situation on the work site. It was also noted that the application of artificial neural networks principles makes the classification process flexible, due to the ability to easily supplement the process with new parameters and neurons. The final stage is analysis of the obtained results. Correctly performed data classification provides an opportunity not only to assess compliance with technological discipline at the site, but also to improve the process of identifying the causes of casting defects. Application of the proposed approach allows us to reduce the influence of human factor in the analysis of thermal conditions of alloys melting and casting with minimal costs for melting monitoring.

Decision Support Capabilities of Telemedicine in Emergency Prehospital Care: Systematic Review.

BackgroundTelemedicine offers a unique opportunity to improve coordination and administration for urgent patient care remotely. In an emergency setting, it has been used to support first responders by providing telephone or video consultation with specialists at hospitals and through the exchange of prehospital patient information. This technological solution is evolving rapidly, yet there is a concern that it is being implemented without a demonstrated clinical need and effectiveness as well as without a thorough economic evaluation.ObjectiveOur objective is to systematically review whether the clinical outcomes achieved, as reported in the literature, favor telemedicine decision support for medical interventions during prehospital care.MethodsThis systematic review included peer-reviewed journal articles. Searches of 7 databases and relevant reviews were conducted. Eligibility criteria consisted of studies that covered telemedicine as data- and information-sharing and two-way teleconsultation platforms, with the objective of supporting medical decisions (eg, diagnosis, treatment, and receiving hospital decision) in a prehospital emergency setting. Simulation studies and studies that included pediatric populations were excluded. The procedures in this review followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement. The Risk Of Bias In Non-randomised Studies–of Interventions (ROBINS-I) tool was used for the assessment of risk of bias. The results were synthesized based on predefined aspects of medical decisions that are made in a prehospital setting, which include diagnostic decision support, receiving facility decisions, and medical directions for treatment. All data extractions were done by at least two reviewers independently.ResultsOut of 42 full-text reviews, 7 were found eligible. Diagnostic support and medical direction and decision for treatments were often reported. A key finding of this review was the high agreement between prehospital diagnoses via telemedicine and final in-hospital diagnoses, as supported by quantitative evidence. However, a majority of the articles described the clinical value of having access to remote experts without robust quantitative data. Most telemedicine solutions were evaluated within a feasibility or short-term preliminary study. In general, the results were positive for telemedicine use; however, biases, due to preintervention confounding factors and a lack of documentation on quality assurance and protocol for telemedicine activation, make it difficult to determine the direct effect on patient outcomes.ConclusionsThe information-sharing capacity of telemedicine enables access to remote experts to support medical decision making on scene or in prolonged field care. The influence of human and technology factors on patient care is poorly understood and documented.

Analysis of Obstacles in The Unloading Process with Failure Mode and Effect Analysis (FMEA) in PT. XYZ

Distraction that occur can have a bad effect on time, financially and product quality. This disorder can be caused by various factors such as the influence of human factors, machinery and production equipment, work environment, and work methods. Based on preliminary observations, many obstacles occur during the unloading process both mechanically and electrically. Thus, an analysis is carried out on the process of unloading wheat from the ship to the silo with FMEA. Based on the analysis after calculating the RPN, the recommendation for problem on silo is to increase the number of silos so that the production process is more efficient. Based on the calculation of the RPN, obtained that resistance at PT. XYZ the weigher to be aware of is the sensor that is often damaged. Recommendations for improvement are regular checks on the sensors weigher. In the unloading process, there are some disturbances that occur so that it is not optimal.

Forecast dynamics of human and technical factors influence on railway automation operational reliability

The correlation of the influence of human and technical factors on railway automation operational reliability has been processed over the past few years. For these purposes, the reports of the Department of Automation and Telecommunications of the Joint Stock Company «Ukrainian Railways» for 2010-2018 were used, as well as materials from relevant publications and scientific conferences. Trends have been formed on the dynamics of human and technical factors influence on the operational reliability of railway automation, which is determined by the failure statistics depending on the influence of various components. Using the methods of quadratic regression and moving average, taking into account the current trends in the dynamics of failures by years, a forecast of the ratio of these factors up to 2030 has been made up. The selected forecasting end date has been determined by the Ukraine’s Transport Strategy approved by the government, in which the specified year is defined as the final one. It has been established that in the absence of comprehensive modernization of the automation and telecommunications facilities in the railway sector, as early as in 2024, the equalization of the influence of human and technical factors on the operational reliability of automation devices is to take place, with the subsequent growth of the technical factor. Accordingly, the conclusion has been made that the problem of modernization of systems and means of automation in railway transport with the use of digitalization technologies for technological processes has become still more urgent

Multi-objective evolutionary algorithms with heuristic decoding for hybrid flow shop scheduling problem with worker constraint

The classical hybrid flow shop scheduling problem (HFSSP) considers the operation and machine constraints but not the worker constraint. Acknowledging the influence and potential of human factors as a key element in improving production efficiency in a real hybrid flow shop, we consider a new realistic HFSSP with worker constraint (HFSSPW) and construct its mixed integer linear programming model. Seven multi-objective evolutionary algorithms with heuristic decoding (HD) (MOEAHs) are proposed to solve the HFSSPW. According to list scheduling, we first present four HD methods for four MOEAHs, and these methods incorporate four priority rules of machine and worker assignments. The earliest due date (EDD) rule is further introduced into the HD methods for the other three MOEAHs. The developed model is solved using CPLEX based on 20 loose instances under a time limit, and the four proposed MOEAHs are evaluated by comparing them with the results from CPLEX and two best-performing algorithms in the literature. The computational results reveal that the proposed MOEAHs perform excellently in terms of the makespan objective. Additionally, comprehensive experiments, including 150 tight instances, are conducted. In terms of solution quality and efficiency, the computational results show that the proposed MOEAHs demonstrate highly effective performance, and integrating EDD into the HD can substantially enhance algorithm performance. Finally, a real-life problem of the foundry plant is solved by MOEAHs and the scheduling solutions totally meet the delivery requirement.

Fast Reconfiguration of Distribution Network Based on Deep Reinforcement Learning Algorithm

In the context of large-scale grid connection of distributed energy, during the reconfiguration of the distribution network, the availability of distributed energy and the load of the distribution system may be inconsistent with the prediction due to the influence of environmental factors and human factors. If the distribution network reconfiguration is still carried out according to the expected offline optimization scheme, there may be reliability problems of voltage over-limits and economic problems of increased network loss in the actual reconfiguration process. Therefore, the reconfiguration plan formulated in advance can give some guidance to the dispatch operator, but it may not be directly used in the actual reconfiguration process. This paper proposes a deep reinforcement learning approach to solving the electric distribution network reconfiguration. Based on the uncertainty of distributed energy output and network load in the distribution network, the online algorithm of distribution network reconfiguration realizes the second-level solution of distribution network reconfiguration, through day-ahead training of the neural network.

Analysis and Classification of Temperature Measurements during Melting and Casting of Alloys Using Neural Networks

In this article, we consider the organizational issues of monitoring the thermal conditions of melting and casting alloys at foundries. It is noted that the least reliable method is when the measuring and fixing the temperature are assigned to the worker. On the other hand, a fully automatic approach is not always available for small foundries. In this regard, the expediency of using an automated approach is shown, in which the measurement is assigned to the worker, while the values are recorded automatically. This method assumes an algorithm implementation for automatic classification of temperature measurements based on an end-to-end array of data obtained in production series. This task solution is divided into three stages. Preparing of raw data for the classification process is provided in the first stage. In the second stage, the task of measurement classification is solved by using principles of artificial neural networks. Analysis of the artificial neural network results has shown its high efficiency and degree of correspondence with the actual situation at the work site. It is also noted that the application of artificial neural networks principles makes the classification process flexible due to the ability to easily supplement the process with new parameters and neurons. The final stage is analysis of the results. Correctly performed data classification provides an opportunity not only to assess agreements with technological efficiency at the site, but also to improve the process of identifying the causes of casting defects. Application of the proposed approach allows us to reduce the influence of human factor in the analysis of thermal conditions of melting and casting alloys with minimal costs for melting monitoring.

Introducing a quality factor into the MES system when calculating machinery production schedule together with measuring equipment

The article is devoted to the inclusion of measuring equipment used to control the geometric parameters of a product after its production in the production schedule recalculation system. The factors that have a negative impact on the production process and subsequent product control are identified. It was revealed that drawing up a production schedule without using the MES system, if it is necessary to manufacture a certain number of products instead of rejected ones, is associated with a high risk. It is indicated that inclusion of measuring equipment and measuring operations in the MES system of the machinery leads to the production process optimization and reduction of the human factor influence. The coefficient characterizing the efficiency of recalculating the production schedule in the case of the inclusion of measuring equipment in the MES-system of the machine park is determined.

Multifrequency Impedance Method Based on Neural Network for Root Canal Length Measurement

Root canal therapy is the most fundamental and effective approach for treating endodontics and periapicalitis. The length of the root canal must be accurately measured to clean the pathogenic substances in it. This study aims to present a multifrequency impedance method based on a neural network for root canal length measurement. A circuit system was designed which generates a current of frequencies from 100 Hz to 20 kHz in order to augment the data of impedance ratios with different combinations of frequencies. Several impedance ratios and other quantified characteristics, such as the type of tooth and file, were selected as features to train a neural network model that could predict the distance between the file and apical foramen. The model uses leave-one-out cross-validation, adopts the Adam optimizer and regularization, and has two hidden layers with nine and five nodes, respectively. The neural network-based multifrequency impedance method exhibits nearly 95% accuracy, compared with the dual-frequency impedance ratio method (which demonstrated no more than 85% accuracy in some situations). This method may eliminate the influence of human and environmental factors on measurement of the root canal length, thereby increasing measurement robustness.

The influence of human factors on 360∘ mulsemedia QoE

Abstract Quality of Experience (QoE) is indelibly linked to the human side of the multimedia experience. Surprisingly, however, there is a paucity of research which explores the impact that human factors has in determining QoE. Whilst this is true of multimedia, it is even more starkly so as far as mulsemedia - applications that involve media engaging three or more of human senses - is concerned. Hence, in the study reported in this paper, we focus on an exciting subset of mulsemedia applications - 360∘ mulsemedia - particularly important given that the upcoming 5G technology is foreseen to be a key enabler for the proliferation of immersive Virtual Reality (VR) applications. Accordingly, we study the impact that human factors such as gender, age, prior computing experience, and smell sensitivity have on 360∘ mulsemedia QoE. Results showed insight into the potential of 360∘ mulsemedia to inspire and to enrich experiences for Generation Z - a generation empowered by rapidly advancing technology. Patterns of prior media usage and smell sensitivity play also an important role in influencing the QoE evaluation - users who have a preference for dynamic videos enjoy and find realistic the 360∘ mulsemedia experiences.

Study on the evolution pattern of groundwater environment in Yishu Basin

This paper proposes the definition of groundwater environment evolution pattern by researching the evolution law of groundwater environment in Yishu Basin, and divides the groundwater environment evolution into five patterns according to the different influence degrees of climate change and human activities. According to the groundwater dynamic monitoring data of the study area from 1980 to 2014, the quantity and quality changes of the groundwater system in Yishu Basin under the influence of climate change and human factors were analyzed, and the groundwater environment evolution pattern was identified. The results showed that the groundwater environment evolution in the study area was mainly affected by climate change before the 1980s, and it was represented by evolution pattern II, the range of evolution pattern I was very small. Since the 1990s, the influence degree of human activities had gradually increased, and the range of evolution pattern III and pattern IV had gradually increased. There had not yet appeared evolution pattern V. The research on the groundwater environment evolution pattern in the study area can scientifically program the groundwater use mode, coordinate the relationship between society and nature, and provide a scientific basis for the area as an emergency water source in Changchun and Jilin.

Multiple Classifier of Traffic Accident Based on Matter-Element Analysis

The paper establishes a new multiple classifier for grading the traffic accident based on the Hard Decision Model and the parallel topological structure. Logistic Regression, decision tree(DT) and BP Neural Network are selected as the base classifiers and matter-element analysis is employed as the fusion algorithm to improve the traditional topological structure. Matter-Element analysis has potential to increase the objectivity and dynamic in the criterion setting of the multiple classifier by combining the confidence coefficient of the base classification results. The accuracies of different base classifiers are used for weight factors calculation instead of the analytic hierarchy process (AHP) or the fuzzy synthetic evaluation (FSE) to avoid the influence of human factors. 200 recordings of the traffic accident are selected as a case study for methodology verification. The results show that compared with the base classifiers, the capacity to identify the class of traffic accident can be strengthened apparently by the multiple classifier based on confidence coefficient and Matter-Element Analysis Meanwhile, this classifier provides the best opportunity to avoid many bad conditions of single model, such as overfitting and under-fitting.

Species of iron in the sediments of the Yellow River and its effects on release of phosphorus.

Species of Fe in the sediments from 21 sections of the Yellow River have been analyzed utilizing the so-called sequential extraction approach. The correlation between iron and phosphorus was analyzed, and the risk of release phosphorus was predicted. The results show that the content of residual iron (Feresidual) in the sediments is the highest, accounting for more than 92.55% of total iron (∑Fe) (the sum of the iron contents in each form is the total iron). Ca-bound phosphorous (PCa) is equal to the sum of authigenic calcium and phosphorus (Paut) and detritus calcium phosphorus (PDe), accounting for more than 73.01% of ∑P, in which PDe accounts for more than 90.91% of PCa, which is the main component of PCa and the main component of ∑P; Fewater in sediments is closely related to Porg, and Feex is closely related to Pex and PAl, which is an important factor to control the release of endogenous phosphorus. The extremely significant positive correlation between Feresidual and Paut and PDe in sediments is that they are affected by complex geological conditions in the Yellow River Basin. Judging by the ratio of ∑Fe/∑P combined with the influence of human factors, the release of phosphorus in sediments of most reaches of the Yellow River is controlled by iron, while the release of phosphorus is inhibited. Sediments in Xigu section H4, Zhongwei section H5, Haibowan section H7, three Sanshenggong section H8, Liulin section H13, and Tongguan section H16 of the Yellow River have certain phosphorus release risks, because phosphorus release is not only controlled by iron but also influenced by human factors, such as industrial and agricultural production level and artificial dam construction.

Function Analysis of Anti-haze Products Based on User Requirements

In recent years, due to the influence of environmental factors and human factors (transportation, heating, etc.), the frequency of haze in China is increasing day by day. And people's protective measures for haze are not in place. This paper uses the method of questionnaire and data analysis to determine people's demand for haze prevention products. 70.13% of the people living in haze areas are willing to buy anti-haze products. In haze weather, 38.7% of the people prefer to use anti haze masks, 27.3% of the people prefer to use air purifiers, and 16.1% of the people prefer to choose anti haze hangers. The two most important aspects that people focus on are haze prevention effect and wearing comfort. Gender differences influence the intention to buy anti haze products. People's basic functional requirements for wearing anti haze products are anti haze effect. Other requirements include wearing comfort, appearance, price and others, and others include sustainability and quality.

Development of model for human factor influence assessment on construction and road machines operation efficiency

Introduction. The human factor and the characteristics of construction and road machine operators, such as experience, work experience, professional skills, skill, etc., have a significant impact on the efficiency of equipment operation. The human factor, on average, is the cause of about a third of the failures of construction and road machines. One of the most effective ways out of this situation is to improve the machines from the point of view of ensuring the compatibility of the elements of the human-machine system. The article considers the issues of the engineering and psychological component of compatibility.Materials and methods. The method of analysis of hierarchies is used, when solving the problem of identifying the causes of operators’ errors and fuzzy logic, to build a model for assessing the impact of the human factor on the efficiency of construction and road machines.Results. As a result of a comprehensive assessment of the causes of errors, it was found that the largest combination of criteria is a group of errors associated with the peculiarities of the task being performed, as well as the properties of the information processed by a person. The developed model for assessing the influence of the human factor on the efficiency of machine operation uses risk as an output variable, and input variables a generalized indicator of the complexity of the algorithm and the level of qualification of the machine operator.Discussion and conclusions. The resulting model allows you to make a primary assessment of the impact of the human factor and maintenance and repair planning, as well as be used in personnel management processes, for example, in terms of sending personnel for training. Further improvement is seen in the development of neurofuzzy anfys models which provide a knowledge base for more effective risk assessment by specific precedents. The structure of the model in terms of input variables for a more correct risk assessment is also possible to be changed.