Search results

Night lighting reflects the prosperous development of economic and the increasingly rich and colorful cultural life. Currently, various technical standards, protocols and management specifications have been developed to build a safe, comfortable and economical lighting environment. However, prevailing evaluation systems focus on objective indexes of illumination and have ignored environmental characteristics and subjective feelings and lacked consideration of regional culture, economic benefit, management and maintenance. In this context, a lighting evaluation system combining subjective and objective is proposed for the first time in this study to explore approaches to guide the development of a healthy and comfortable urban night-time environment.

Existing research and relevant lighting standards are analyzed and an evaluation model with a logical hierarchy is constructed by combining with the evaluation theory that is set based on people and the environment. The index weights were scientifically determined on the basis of the analytic hierarchy process (AHP) and fuzzy comprehensive evaluation method. The rationality and validity of the proposed evaluation system is verified in accordance with field projects and case studies.

Taking into account traditional and cultural factors, the evaluation model established has an acceptable accuracy. Evaluation based on subjective-objective combination can provide a scientific basis for the management and optimization of night lighting.

The proposed evaluation system can serve as a guiding reference for other areas of cultural identity and esthetic perspective.

The purpose of this paper was to compare the response of selected hybrid Fibre Metal Laminates (FMLs) in the form of glass and carbon fibre aluminium laminates to dynamic and static loads compared together.

The subject of examination was FMLs (Al/CFRP and Al/GFRP). The samples were subjected to low-velocity impact and quasi-static indentation. The response of laminates to the both types of loads was evaluated by comparison of force – displacement diagrams including the values of maximum forces as well as the extent and nature of structure degradation as a result of loads.

In case of Al/GFRP laminates, the analysis of characteristic relations, i.e. force – displacement and the impactor influence area in case of indentation and impact confirmed that certain parameters, i.e. the values of maximum force transferred by laminate, destruction surface area and destruction mechanisms are consistent after static and dynamic tests. Significant differences were found in destruction scale in Al/GFRP laminates despite considerable fitting of force – displacement diagrams to static and dynamic tests. Destruction surface area observed in FML carbon laminates subjected to dynamic loads was significantly smaller than after indentation but perforation area occurring at the unloaded side was much more extensive.

Research issues in the scope of dynamic loads by means of concentrated force in composite materials and interpretation of the effects of their impacts are extremely complex. Therefore, the attempts are made to predict the resistance to dynamic loads by means of concentrated force using statistical research methods. The test results might be useful for the design and simulations of FMLs applications in aerospace.

From the analysis of available literature, it appears that there are no studies exploring the issue of forecasting or comparison the effects of static and dynamic tests for hybrid FMLs. The new hybrid materials like FMLs have different mechanisms of damage initiation and propagation as a result of impact, in comparison to classic composite materials. It means that possibilities of using the static loads to predict impact resistance should be known well for all type of FMLs. Actually, there is no research about static indentation in relation to low-velocity impact of aluminium-carbon laminates. This situation encouraged the authors of the present study to undertake research in this scope. The results can demonstrate and explain why prediction of impact resistance of FMLs by using static indentation is uncertain and not always valuable.

The purpose of this paper is to predict quantitative level of stray current leaking to the buried metallic structure by establishing convolution neural network (CNN) model.

First, corrosion experimental system of buried metallic structure is established. The research object of this paper is the polarization potential within 110 min, CNN model is used to predict the quantitative level of stray current leakage using the data from corrosion experimental system further. Finally, results are compared with the method using BP neural network.

Results show that the CNN model has better predictive effect and shorter prediction time than the BP model, the accuracy of which is 82.5507 per cent, and the prediction time is shortened by more than 10 times.

The established model can be used to forecast the level of stray current leakage in the subway system effectively, which provides a new theoretical basis for evaluating the stray current corrosion hazard of buried metallic structure.

Recognizing every worker's working status instead of only describing the existing construction activities in static images or videos as most computer vision-based approaches do; identifying workers and their activities simultaneously; establishing a connection between workers and their behaviors.

Taking a reinforcement processing area as a research case, a new method for recognizing each different worker's activity through the position relationship of objects detected by Faster R-CNN is proposed. Firstly, based on four workers and four kinds of high-frequency activities, a Faster R-CNN model is trained. Then, by inputting the video into the model, with the coordinate of the boxes at each moment, the status of each worker can be judged.

The Faster R-CNN detector shows a satisfying performance with an mAP of 0.9654; with the detected boxes, a connection between the workers and activities is established; Through this connection, the average accuracy of activity recognition reached 0.92; with the proposed method, the labor consumption of each worker can be viewed more intuitively on the visualization graphics.

With this proposed method, the visualization graphics generated will help managers to evaluate the labor consumption of each worker more intuitively. Furthermore, human resources can be allocated more efficiently according to the information obtained. It is especially suitable for some small construction scenarios, in which the recognition model can work for a long time after it is established. This is potentially beneficial for the healthy operation of the entire project, and can also have a positive indirect impact on structural health and safety.

This paper summarizes the severity of global warming, collaboration and endeavor within international government and the trend of international development for “energy-saving and emission reduction.” The Chinese government is enduring high pressure under the environment of “global warming” and “energy-saving and emission reduction” and it has made a policy for “energy-saving and emission reduction.” Based on this, we analyzed the possibility and feasibility for our logistics to “energy-saving and emission reduction,” then propose some solutions for our logistics industry to development and “energy-saving and emission reduction.”

Online employee reviews have emerged as a crucial information source for business managers to evaluate employee behavior and firm performance. The purpose of this paper is to test the relationship between employee personality traits, derived from online employee reviews and job satisfaction and turnover behavior at the individual level.

The authors apply text-mining techniques to extract personality traits from online employee reviews on Indeed.com based on the Big Five theory. They also apply a machine learning classification algorithm to demonstrate that incorporating personality traits can significantly enhance employee turnover prediction accuracy.

Personality traits such as agreeableness, conscientiousness and openness are positively associated with job satisfaction, while extraversion and neuroticism are negatively related to job satisfaction. Moreover, the impact of personality traits on overall job satisfaction is stronger for former employees than for current employees. Personality traits are significantly linked to employee turnover behavior, with a one-unit increase in the neuroticism score raising the probability of an employee becoming a former employee by 0.6%.

These findings have implications for firm managers looking to gain insights into employee online review behavior and improve firm performance. Online employee review websites are recommended to include the identified personality traits.

This study identifies employee personality traits from automated analysis of employee-generated data and verifies their relationship with employee satisfaction and employee turnover, providing new insights into the development of human resources in the era of big data.

Applications of robotic systems in agriculture, forestry and high-altitude work will enter a new and huge stage in the near future. For these application fields, climbing robots have attracted much attention and have become one central topic in robotic research. The purpose of this paper is to propose an energy-optimal motion planning method for climbing robots that are applied in an outdoor environment.

First, a self-designed climbing robot named Climbot is briefly introduced. Then, an energy-optimal motion planning method is proposed for Climbot with simultaneous consideration of kinematic constraints and dynamic constraints. To decrease computing complexity, an acceleration continuous trajectory planner and a path planner based on spatial continuous curve are designed. Simulation and experimental results indicate that this method can search an energy-optimal path effectively.

Climbot can evidently reduce energy consumption when it moves along the energy-optimal path derived by the method used in this paper.

Only one step climbing motion planning is considered in this method.

With the proposed motion planning method, climbing robots applied in an outdoor environment can commit more missions with limit power supply. In addition, it is also proved that this motion planning method is effective in a complicated obstacle environment with collision-free constraint.

The main contribution of this paper is that it establishes a two-planner system to solve the complex motion planning problem with kinodynamic constraints.

This paper aims to thrash out friction and wear properties of automobile brake lining reinforced by lignin fiber and glass fiber in braking process.

ABAQUS finite element software was used to analyze thermo-mechanical coupled field of friction materials. XD-MSM constant speed friction testing machine was used to test friction and wear properties of friction material. Worn surface morphology and mechanism of friction materials were observed by using scanning electron microscope.

The results show that when the temperature was below 350°C, worn mechanism of MFBL was mainly fatigue wear and abrasive wear, and worn mechanism of GFBL was mainly fatigue wear because MFBL contained lignin fiber. Therefore, it exhibits better mechanical properties and friction and wear properties than those of GFBL.

Lignin fiber can improve mechanical properties and friction and wear properties of the automobile brake lining.

Task recommendation is an important way for workers and requesters to get better outcomes in shorter time in crowdsourcing. This paper aims to propose an approach based on 2-tuple fuzzy linguistic method to recommend tasks to the workers who would be capable of completing and accept them.

In this paper, worker’s capability-to-complete (CTC) and possibility-to-accept (PTA) for a task needs to be recommended are proposed, measured and aggregated to determine worker’s priority for task recommendation. Therein, the similarity between the recommended task and its similar tasks and worker’s performance on these similar tasks are computed and aggregated to determine worker’s CTC quantitatively. In addition, two factors of worker’s active degree and worker’s preferences to a task category are presented to reflect and determine worker’s PTA. In the process of measuring them, 2-tuple fuzzy linguistic method is used to represent, process and aggregate vague and imprecise information.

To demonstrate the implementation process and performance of the proposed approach, an illustrative example is conducted on Taskcn, a widely used Chinese online crowdsourcing market. The experimental results show that the proposed approach outperformed the self-selection approach, especially for complex or creative tasks. Moreover, comparing with task recommendation considering worker’s CTC solely, the proposed approach would be better in terms of workers’ response rate. Additionally, the use of linguistic terms and fuzzy linguistic method facilitates the expression of vague and subjective information and makes recommendation process more practical.

In the study, the authors capture alternative workers, collect workers’ behaviors and compute workers’ CTC and PTA manually. However, as the number of tasks and alternative workers grow, the issue, i.e. how to conveniently collect workers’ behaviors and determine their CTC and PTA, becomes conspicuous and needs to be studied further.

The proposed approach provides an alternative way to perform tasks posted in crowdsourcing platforms. It can assist workers to contribute to right tasks, and requesters to get outcomes with high quality more efficiently.

This study proposes an approach to task recommendation in crowdsourcing that integrates workers’ CTC and PTA for the recommended tasks and can deal with vague and imprecise information.