Towards an efficient indexing and searching model for service discovery in a decentralised environment.

Given the growth and outreach of new information, communication, computing and electronic technologies in various dimensions, the amount of data has explosively increased in the recent years. Centralised systems suffer some limitations to dealing with this issue due to all data is stored in central data centres. Thus, decentralised systems are getting more attention and increasing in popularity. Moreover, efficient service discovery mechanisms have naturally become an essential component in both large-scale and small-scale decentralised systems and. This research study is aimed at modelling a novel efficient indexing and searching model for service discovery in decentralised environments comprising numerous repositories with massive stored services. The main contributions of this research study can be summarised in three components: a novel distributed multilevel indexing model, an optimised searching algorithm and a new simulation environment. Indexing model has been widely used for efficient service discovery. For instance; the inverted index is one of the popular indexing models used for service retrieval in consistent repositories. However, redundancies are inevitable in the inverted index which is significantly time-consuming in the service discovery and retrieval process. This theeis proposes a novel distributed multilevel indexing model (DM-index), which offers an efficient solution for service discovery and retrieval in distributed service repositories comprising massive stored services. The architecture of the proposed indexing model encompasses four hierarchical levels to eliminate redundancy information in service repositories, to narrow the searching space and to reduce the number of traversed services whilst discovering services. Distributed Hash Tables have been widely used to provide data lookup services with logarithmic message costs which only require maintenance of limited amounts of routing states. This thesis develops an optimised searching algorithm, named Double-layer No-redundancy Enhanced Bi-direction Chord (DNEB-Chord), to handle retrieval requests in distributed destination repositories efficiently. This DNEB-Chord algorithm achieves faster routing performances with the double-layer routing mechanism and optimal routing index. The efficiency of the developed indexing and searching model is evaluated through theoretical analysis and experimental evaluation in a newly developed simulation environment, named Distributed Multilevel Bi-direction Simulator (DMBSim), which can be used as cost efficient tool for exploring various service configurations, user retrieval requirements and other parameter settings. Both the theoretical validation and experimental evaluations demonstrate that the service discovery efficiency of the DM-index outperforms the sequential index and inverted index configurations. Furthermore, the experimental evaluation results demostrate that the DNEB-Chord algorithm performs better than the Chord in terms of reducing the incurred hop counts. Finally, simulation results demonstrate that the proposed indexing and searching model can achieve better service discovery performances in large-scale decentralised environments comprising numerous repositories with massive stored services.N/

A novel multilevel index model for distributed service repositories.

This study, based on the theory of equivalence relations, proposes a novel multilevel index model for decentralized service repositories to eliminate redundant information and enhance the time-management quality of the service retrieval process of the service repository architecture. An efficient resource discovery algorithm based on Discrete Hash Tables, is presented to enable efficient and effective retrieval services among different distributed repositories. The performance of the proposed model and the supporting algorithms have been evaluated in a distributed environment. Experimental results validate the effectiveness of our proposed indexing model and search algorithm.N/

An efficient indexing model for the fog layer of industrial Internet of Things.

Fog Computing is gaining popularity and is being increasingly deployed in various latency-sensitive application domains including industrial IoTs. However, efficient discovery of services is one of the prevailing issues in the fog nodes of indus-trial IoTs which restrain their efficiencies in availing appropriate services to the clients. To address this issue, this paper proposes a novel effi-cient multilevel index model based on equivalence relation, named the DM-index model, for service maintenance and retrieval in the fog layer of industrial IoTs to eliminate redundancy, narrow the search space, reduce both the traversed number of services and retrieval time, ultimately to improve the service discovery efficiency. The efficiency of the proposed index model has been verified theoretically and evaluated experimentally, which demonstrates that the proposed model is effective in achieving much better service discovery and retrieval performance than the sequential and inverted index models.N/

New Technology for Preventing and Controlling Air Leakage in Goaf Based on the Theory of Wind Flow Boundary Layer

Air leakage in the goaf is a fundamental reason for the high-temperature heat damage of the coal mining face and the gas concentration in the upper corner exceeding the limit. Combined with the boundary layer theory, this study analyzes the airflow state of the coal mining face. We propose installing a new air curtain system to prevent air leakage in the goaf. The length of the intake air curtain is determined by solving the theoretical equation. Numerical simulation is used to study different layout schemes of air curtains, and the spatial distribution law of different air volumes inside the working face is analyzed. The simulation results are compared with the field-measured data. The results show that when the length of the air curtain on the air inlet side is 20 m, the wind flow on the working face can be approximated as a state of attached jet, and a diffuse turbulent flow area will be formed outside the air curtain. Gas concentration will increase in this area. The air leakage prevention effect is best when the air curtains with a length of 20 m for the inlet air and 10 m for the return air are arranged at both ends of the working face. This air curtain system can reduce the temperature of the working face and the gas concentration in the upper corner and has certain guiding significance for the air leakage prevention work in the goaf

Preparation and Performance Test of the Super-Hydrophobic Polyurethane Coating Based on Waste Cooking Oil

In order to solve the problem of dust accumulation on the fin surface of a mine air cooler, a method of preparing super-hydrophobic polyurethane (SPU) coating based on waste cooking oil (WCO) was proposed. Firstly, the polyurethane prepolymer was synthesized with WCO as a raw material, and then the polyurethane prepolymer was modified with amino-terminated polydimethylsiloxane (ATP) to obtain SPU emulsion. The chemical structure and thermal stability of SPU were characterized by infrared spectrum and thermogravimetric analysis. A series of nanocomposites were prepared by combining modified silicon carbide (APT-SiC) particles and SPU emulsions. According to the parameters of pull-off strength, contact angle, sliding angle and thermal conductivity, the filler ratio of nanocomposites was optimized. The test results show that when the content of APT-SiC particles is 20 wt %, super-hydrophobic polyurethane coating can be obtained. The coating has good pull- off strength and thermal conductivity, and the contact angle and sliding angle are 161° and 3°, respectively. In addition, the practical application of the super-hydrophobic polyurethane coating was tested by related experiments. The experimental results show that the coating has good self-cleaning, wear resistance and anti-corrosion performance, can meet the requirements of air coolers in special environments, and has great application prospects

Research on a Risk Early Warning Mathematical Model Based on Data Mining in China’s Coal Mine Management

The degree of informatization of coal mine safety management is becoming higher and higher, and a large amount of information is generated in this process. How to convert the existing information into useful data for risk control has become a challenge. To solve this challenge, this paper studies the mathematical model of coal mine risk early warning in China based on data mining. Firstly, the coal mine risk data was comprehensively analyzed to provide basic data for the risk prediction model of data mining. Then, the adaptive neuro-fuzzy inference system (ANFIS) was optimized twice to build the coal mine risk prediction model. By optimizing the calculation method of the control chart, the coal mine risk early warning system was proposed. Finally, based on the coal mine risk early warning model, the software platform was developed and applied to coal mines in China to control the risks at all levels. The results show that the error of the optimized ANFIS was reduced by 66%, and the early warning error was reduced by 57%. This study aimed to provide implementation methods and tools for coal mine risk management and control, and data collected has reference significance for other enterprises

A Novel Efficient Index Model and Modified Chord Protocol for Decentralized Service Repositories

Distributed service repository systems are now being deployed widely rather than the traditional centralized service repository systems, issues such as information redundancy and service failures are still prevailing in existing models and search algorithms. Inverted index structure is a widely adopted model for service management and retrieval in service repositories, but the inverted index shows considerable redundancies which can significantly increase the retrieval time in large-scale distributed repositories. Chord is a widely used protocol for service retrieval in structured P2P networks. However, it is not sufficiently abundant for efficient service discovery and retrieval among all the nodes, since each node’s finger table only contains the clockwise direction key-node information. To this end, this paper develops a multilevel index model for service maintenance and retrieval for decentralized service repositories, for the purpose of reducing the service retrieval time. In addition, a modified algorithm, called MLBChord, is deployed in this paper, for improving the search efficiency. Experiments conducted on a realistic simulation environment shows that our developed model reduces the retrieval time more than 50% to that Chord with inverted index, thus achieves an effective search retrieval time

Coal and Gas Outburst Risk Prediction and Management Based on WOA-ELM

A gas outburst risk level prediction method, based on the Whale Optimization Algorithm (WOA) Improved Extreme Learning Machine (ELM), is proposed to predict the coal and gas outburst hazard level more accurately. Based on this method, recommendations are given according to the gas outburst risk level with the help of the Case-Based Reasoning (CBR) method. Firstly, we analyze the accident reports of gas outburst accidents, select the gas outburst risk prediction index, and construct the gas outburst risk prediction index system by combining the gas outburst prevention and control process. The WOA-ELM model was used to predict the gas outburst risk level by selecting data from 150 accident reports from 2008 to 2021. Again, based on the coal and gas outburst risk level, CBR is used to match the cases and give corresponding suggestions for different levels of gas outburst risk conditions to help reduce the gas outburst risk. The results show that the WOA-ELM algorithm has better performance and faster convergence than the ELM algorithm, when compared in terms of accuracy and the error of gas outburst hazard prediction. The use of CBR to manage prediction results can be helpful for decision-makers

A Dynamic Self-Attention-Based Fault Diagnosis Method for Belt Conveyor Idlers

Idlers are typical rotating parts of a belt conveyor carrying the conveyor belt and materials. The complex operating noise and unstable features lead to poor accuracy of sound-based idler fault diagnosis. This paper proposes a fault diagnosis method for belt conveyor idlers based on Transformer’s dynamic self-attention (DSA). Firstly, the A-weighted time-frequency spectrum of the idler sound is extracted as the input. Secondly, based on the DSA block, the multi-frequency cross-correlation DSA algorithm is designed to extract the cross-correlation features between different frequency bands in the input feature map, and the global DSA algorithm is applied to perceive and enhance the global correlation features in parallel. Finally, the cross-correlation and global correlation features are concatenated and linearly projected into a fault-type space to diagnose typical bearing and roller faults of idlers. The method makes full use of the relevant information scattered in different frequency bands of the idler running sound under complex working conditions and reduces the negative effect of the strong running noise on the extraction of weak fault features. Experimental results show that the fault diagnosis accuracy is 94.6% and the latency is 27.8 ms