Public Places Safety Management Evaluation of Railway Stations

AbstractWith more and more attentions paid to safety problems in public places by the whole society, preventions and controls of unexpected events in the crowded places of railway stations are especially important. Aimed at the safety problems in railway station, such as crowded people, complex environment, weak management and so on, in order to make the public places safety management of railway stations more effective, through analysis of public places system safety features and hidden dangers of railway stations, public places safety management evaluation indicators system is constructed and aimed at every specific indicator. Corresponding safety management and control requirements are put forward. Taking Xi’an Railway Station as the example, Analytic Hierarchy Process (AHP) is used to get indicators weight values. Public places safety main control factor is obtained by analysis. According to the evaluation results, aimed at the weak links in safety management, improvement measures are put forward, supplying an important basis of perfect safety management system and improvement of safety management

Salient Object Detection via Integrity Learning

Albeit current salient object detection (SOD) works have achieved fantastic progress, they are cast into the shade when it comes to the integrity of the predicted salient regions. We define the concept of integrity at both the micro and macro level. Specifically, at the micro level, the model should highlight all parts that belong to a certain salient object, while at the macro level, the model needs to discover all salient objects from the given image scene. To facilitate integrity learning for salient object detection, we design a novel Integrity Cognition Network (ICON), which explores three important components to learn strong integrity features. 1) Unlike the existing models that focus more on feature discriminability, we introduce a diverse feature aggregation (DFA) component to aggregate features with various receptive fields (i.e.,, kernel shape and context) and increase the feature diversity. Such diversity is the foundation for mining the integral salient objects. 2) Based on the DFA features, we introduce the integrity channel enhancement (ICE) component with the goal of enhancing feature channels that highlight the integral salient objects at the macro level, while suppressing the other distracting ones. 3) After extracting the enhanced features, the part-whole verification (PWV) method is employed to determine whether the part and whole object features have strong agreement. Such part-whole agreements can further improve the micro-level integrity for each salient object. To demonstrate the effectiveness of ICON, comprehensive experiments are conducted on seven challenging benchmarks, where promising results are achieved

Continual All-in-One Adverse Weather Removal with Knowledge Replay on a Unified Network Structure

In real-world applications, image degeneration caused by adverse weather is always complex and changes with different weather conditions from days and seasons. Systems in real-world environments constantly encounter adverse weather conditions that are not previously observed. Therefore, it practically requires adverse weather removal models to continually learn from incrementally collected data reflecting various degeneration types. Existing adverse weather removal approaches, for either single or multiple adverse weathers, are mainly designed for a static learning paradigm, which assumes that the data of all types of degenerations to handle can be finely collected at one time before a single-phase learning process. They thus cannot directly handle the incremental learning requirements. To address this issue, we made the earliest effort to investigate the continual all-in-one adverse weather removal task, in a setting closer to real-world applications. Specifically, we develop a novel continual learning framework with effective knowledge replay (KR) on a unified network structure. Equipped with a principal component projection and an effective knowledge distillation mechanism, the proposed KR techniques are tailored for the all-in-one weather removal task. It considers the characteristics of the image restoration task with multiple degenerations in continual learning, and the knowledge for different degenerations can be shared and accumulated in the unified network structure. Extensive experimental results demonstrate the effectiveness of the proposed method to deal with this challenging task, which performs competitively to existing dedicated or joint training image restoration methods. Our code is available at https://github.com/xiaojihh/CL_all-in-one

De-noising method of mine gas monitoring data

In order to solve problem of easily eliminating effective signal component of mine gas monitoring data by wavelet de-noising, a de-noising method of mine gas monitoring data based on Hilbert-Huang transform was proposed. Original gas monitoring data sequence is decomposed into a set number of intrinsic mode function components by using empirical mode decomposition, and marginal spectrum is obtained through Hilbert transform. Correlation between the original sequence and each intrinsic mode function component is analyzed according to amplitude frequency relationship in the marginal spectrum between the original signal and each intrinsic mode function component, so as to determine and eliminate noise signal sequence. The case analysis shows that characteristics of the gas monitoring data is clear in time scale by empirical mode decomposition, which is good for identifing high frequency noise of the signal easily. The high frequency noise of gas monitoring data is eliminated through Hilbert spectrum analysis, and intrinsic characteristic of the original gas monitoring data is retained, which avoids signal distortion while de-noising is achieved, and maintains authenticity of the gas monitoring data

Characteristic analysis and preprocessing of mine gas monitoring data

In view of characteristics of abnormal data, missing data and noisy data of mine gas monitoring data, a preprocessing method of gas monitoring data was proposed. Abnormal data is replaced by use of moving average line processing method or auto regressive model processing method, missing data is filled by employing cubic exponential smoothing method and data denoising is processed though wavelet soft threshold method. The case analysis shows that the method can eliminate interference of abnormal data, ensure integrity of monitoring data and smooth characteristic curve of monitoring data without changing statistical characteristics of gas monitoring data

Pre-warning method of gas concentration based on correlation analysis of monitoring data

A pre-warning method of gas concentration based on correlation analysis of monitoring data was proposed. Firstly, statistical characteristic of mine gas monitoring data is analyzed, intrinsic correlation characteristic of time series consisted by the gas monitoring data is researched by use of grey correlation analysis method. Further, pre-warning index and pre-warning threshold are determined based on real-time monitoring data. Then abnormal situation of gas concentration is analyzed and pre-warning level is divided, so as to realize dynamic and quantitative pre-warning. The case analysis shows that the method is applicability for gas concentration pre-warning of single monitoring site, which can provide effective decision basis for daily safety management of coal mine

Research on grading control of mine ventilation system

In view of problem that present mine ventilation system control had not been considered from overall perspective of ventilation system, a grading control method of mine ventilation system was proposed based on structure of mine ventilation system and air volume distribution characteristics. Firstly, deviation value of ventilation condition parameters of each ventilation location is obtained by comparing real-time acquisition value and safety setting value of the ventilation condition parameters. Then the deviation value is online adjusted and modified by use of automatic correction controller, which is transmitted to PID controller to obtain ventilation condition adjustment parameters. At last, frequency converter of main fan power supply module, electric air door and air-filled air bag at the end of compressed air pipeline are respectively controlled by intelligent control module according to the ventilation condition adjustment parameters, so as to realize dynamic adjustment of air volume at three levels, which are air volume control of whole mine, air volume control of mining area and air volume control of working face. The control method meets requirements of sequential, systematic and coherent regulation of air volume in ventilation system

Effect of processing parameters of rotary ultrasonic machining on surface integrity of potassium dihydrogen phosphate crystals

Potassium dihydrogen phosphate is an important optical crystal. However, high-precision processing of large potassium dihydrogen phosphate crystal workpieces is difficult. In this article, surface roughness and subsurface damage characteristics of a (001) potassium dihydrogen phosphate crystal surface produced by traditional and rotary ultrasonic machining are studied. The influence of process parameters, including spindle speed, feed speed, type and size of sintered diamond wheel, ultrasonic power, and selection of cutting fluid on potassium dihydrogen phosphate crystal surface integrity, was analyzed. The surface integrity, especially the subsurface damage depth, was affected significantly by the ultrasonic power. Metal-sintered diamond tools with high granularity were most suitable for machining potassium dihydrogen phosphate crystal. Cutting fluid played a key role in potassium dihydrogen phosphate crystal machining. A more precise surface can be obtained in machining with a higher spindle speed, lower feed speed, and using kerosene as cutting fluid. Based on the provided optimized process parameters for machining potassium dihydrogen phosphate crystal, a processed surface quality with R a value of 33 nm and subsurface damage depth value of 6.38 μm was achieved