Analysis of Digital Long Jump Take-off Wearable Sensor Monitoring System

The wearable sensor monitoring system builds a long jump take-off recognition network model based on different digital feature extraction methods (one-dimensional digital feature extraction method, two-dimensional digital feature extraction method, and feature extraction method combining one-dimensional digitization and recursion). Experimental verification and analysis are performed on the processed sample data, and the identification effects, advantages, and disadvantages of the four methods are obtained. First, the sensor behavior movement collection software is designed based on the Android system, and the collection time and frequency are specified at the same time. In addition, for the problem of multisensor behavior recognition, an effective result fusion method is proposed. In a multisensor behavior recognition system, constructing a parallel processing architecture is conducive to improving the rate of behavior recognition. To maintain or increase the rate of behavior recognition, the result fusion method plays a vital role. Finally, this paper analyzes the process of multitask behavior recognition and constructs a residual model that can effectively integrate multitask results and fully mine data information. The experimental results show that, for the monitoring of exercise volume, we use step count statistics to extract feature values that can distinguish activity types based on human motion characteristics. This paper proposes a sample autonomous learning method to find the optimal sample training set and avoid occurrence of overfitting problems. In the recognition of 11 types of long jump take-offs, the average accuracy rate reached 98.7%. The average replacement method is used to count the number of steps, which provides a data reference for the user’s daily exercise volume.

Introduction of IoT-Based Surrogate Parameters in the Ex-Post Countermeasure of Industrial Sectors in Integrated Permit Policy

New approaches and techniques are required for environmental management in workplaces that emit pollutants. The best available techniques (BATs) that are suggested by the integrated environmental permit system (IEPS) play a pivotal role in providing state-of-the-art approaches and techniques to workplaces. For the workplaces that install facilities with the BATs, it is necessary to perform real-time monitoring to determine whether pollutant emission facilities and treatment facilities are operating normally and whether such facilities can maintain the optimal condition during the emission of pollutants. The environmental management measures used before the IEPS had limitations for workplaces that are subjected to integrated environmental management because they consider the entire workplace as a single pollution source by combining all media. In addition, only the emission concentrations and amounts of pollutants were monitored because only the points at which most pollutants are discharged, i.e., stacks for air pollutant emission facilities and final effluent outlets for water pollutant emission facilities, are monitored. These monitoring approaches in a variety of facilities involve considerable costs, and most of them (95% for stacks in workplaces under IEPS, Korea) cannot perform real-time observation even though some of them are capable of it (5% for stacks managed by Tele Monitoring System, TMS). In this study, the applicability of a wireless sensor monitoring network was examined as a new approach for environmental management in workplaces. Based on the sensor monitoring network, surrogate parameters that can indirectly monitor the critical operating factors of pollutant emission and prevention facilities were developed and then measured in real time to examine the status of these facilities. Surrogate parameters can be used instead of directly measuring pollutants as the former has high efficiency, such as cost reduction, and can provide reliable data on the emission characteristics of pollutants. The wireless network is based on an Internet of Things (IoT) platform under real-time conditions in the emission and production facilities in a workplace. This study suggested that a wireless network using the IoT platform improves the BAT assessment methodology to understand the efficiency of environmental pollution reduction and the economic contribution to the workplace.

Model Test on Mechanical and Deformation Property of a Geomembrane Surface Barrier for a High Rockfill Dam

The cushion of a geomembrane surface barrier of a high rockfill dam built on deep overburden is prone to crack and fail because of excessive flexural deformation. This study proposes a geomembrane surface barrier for a high rockfill dam on deep overburden. The proposed geomembrane surface barrier uses polyurethane bonded aggregates as the cushion material. The loading and deformation performance of the barrier system under uniform water pressure was investigated using a self-developed structure model test device. The mechanical and deformation property of each layer of the barrier, and the interaction mode between adjacent layers, were obtained through external videos and internal sensor monitoring. The results demonstrated that the polyurethane bonded aggregate cushion exhibited good adaptability to flexural deformation during the entire loading process and maintained good contact and coordinate deformation with the upper protective and the lower transition layers. The geomembrane surface barrier created using polyurethane bonded aggregates as the cushion material can adapt to the flexural deformation of a high rockfill dam surface on deep overburden.

Special Vehicles to Prevent Touching High-voltage Lines Monitoring and Warning Device

Domestic regional special vehicles and construction personnel high-voltage electrocution accidents always occur, resulting in large-scale equipment and socio-economic losses. Based on this, in order to effectively reduce special vehicles touching high-voltage lines causing safety accidents, this paper researches and designs a safety monitoring and warning device for special vehicles touching high-voltage lines, mainly including sensor monitoring templates, signal transmission templates and monitoring and warning alert templates, through the decoding of different template principles, combined with electric field induction and wireless transmission principles, the safety distance of high-voltage lines is relevant to explore, then when Special vehicle objects into the safety distance threshold, that is, to start the information monitoring alarm, to remind the construction of special vehicle equipment and personnel away from electrocution, to ensure safe production. Through the monitoring and warning device, the rationalization, economy and safety of high-voltage line can be achieved.

Accurate Identification of Agricultural Inputs Based on Sensor Monitoring Platform and SSDA-HELM-SOFTMAX Model

The unreliability of traceability information on agricultural inputs has become one of the main factors hindering the development of traceability systems. At present, the major detection techniques of agricultural inputs were residue chemical detection at the postproduction stage. In this paper, a new detection method based on sensors and artificial intelligence algorithm was proposed in the detection of the commonly agricultural inputs in Agastache rugosa cultivation. An agricultural input monitoring platform including software system and hardware circuit was designed and built. A model called stacked sparse denoising autoencoder-hierarchical extreme learning machine-softmax (SSDA-HELM-SOFTMAX) was put forward to achieve accurate and real-time prediction of agricultural input varieties. The experiments showed that the combination of sensors and discriminant model could accurately classify different agricultural inputs. The accuracy of SSDA-HELM-SOFTMAX reached 97.08%, which was 4.08%, 1.78%, and 1.58% higher than a traditional BP neural network, DBN-SOFTMAX, and SAE-SOFTMAX models, respectively. Therefore, the method proposed in this paper was proved to be effective, accurate, and feasible and will provide a new online detection way of agricultural inputs.

On multi-sensor monitoring of fiber laser fusion cutting

Laser cutting is a well-established industrial process for sheet metal applications. However, cutting thick plates is still accompanied by problems because of the characteristic limited process parameter window. Since cutting by means of fiber lasers has become dominant, tailored solutions are required in such systems for industrial applications. The development of a robust real-time monitoring system, which adapts the process parameters according to a specific quality requirement, implies a significant step forward towards automated laser cutting and increases the process robustness and performance. In this work, a coaxial multi-sensor monitoring system is tested for fiber laser cutting of stainless steel thick plates. A high-speed camera and a photodiode sensor have been selected for this investigation. Experiments at different cutting speeds, representing primary cut quality cases, have been conducted and various features of the obtained process zone signals have been examined. Finally, the feasibility of industrial application of the developed setup for high-power fiber laser cutting is discussed, followed by several implementation recommendations.

A study on network analysis by navigation of robots using graphical measures to solve the complexity

In this paper, we present a graph theoretical analysis of a procedure for maintaining communication for tasks such as monitoring, identification, completion and productivity, where connectivity between the components involved in the task is required for conditional alertness. We present graphical measures and fuzzy theoretical tools to analyse the communication between the components in a network undergoing different tasks and accumulate a suitable information regarding the activities of the components by observing through sensor monitoring system. Also, we consider the efficiency of the robots in the monitoring system to control the position and orientation relative to other components to sustain communication links.

Efficient Mining of Frequent Item Sets on Large Uncertain Databases

In applications like location-based services, sensor monitoring systems and data integration diligence the data manipulated is highly ambiguous. mining manifold itemsets from generous ambiguous database illustrated under possible world semantics is a crucial dispute. Mining manifold Itemsets is technically brave because the ambiguous database can accommodate a fractional number of possible worlds. The mining process can be formed as a Poisson binomial distribution, by noticing that an Approximated algorithm is established to ascertain manifold Itemsets from generous ambiguous database exceedingly. Preserving the mining result of scaling a database is a substantial dispute when a new dataset is inserted in an existing database. In this paper, an incremental mining algorithm is adduced to retain the mining consequence. The cost and time are reduced by renovating the mining result rather than revising the whole algorithm on the new database from the scrap. We criticize the support for incremental mining and ascertainment of manifold Itemsets. Two common ambiguity models in the mining process are Tuple and Attribute ambiguity. Our approach reinforced both the tuple and attribute uncertainty. Our accession is authorized by interpreting both real and synthetic datasets.