Waste Of Manpower Research Articles

Modeling process for full forming sports underwear

Abstract Most of the sports underwear available is sewn using cutting and sewing techniques. During the exercise process, it is easy to cause discomfort to the skin due to repeated friction at the seam, and at the same time, it is also a waste of manpower and raw material costs. In order to address these issues present in existing sports underwear, during its design and development, combined with fully forming knitting technology, construction process model, so as to realize the integrated knitting of sports underwear. First of all, through the characteristics of the local style of sports underwear, the key points of its prototype were probed. Second, on this basis, its process model was established, including the forming methods and knitting rules of the body, shoulder collar, and bottom circumference. Finally, the feasibility of the technological model is proved by the braiding of a fully formed sports underwear. The change in the body size can be achieved by using the two-side braiding transfer method. The curve contour of the shoulder and neck can be obtained by using the triangular polyline segmentation method. The bottom part is divided into a cylinder part and a supporting part. The local braiding method is used to achieve the knitting. This study puts forward a process model and application method of fully formed sports underwear, which provides a certain method and reference for the design and production of fully formed sports underwear.

Waiting Time Control Chart for M/G/1 Retrial Queue

Retrial queues are used extensively to model many practical problems in service systems, call centers, data centers, and computer network systems. The average waiting time is the main observable characteristic of the retrial queues. Long queues may cause negative impacts such as waste of manpower and unnecessary crowding leading to suffocation, and can even cause trouble for customers and institutions. Applying control chart technology can help managers analyze customers’ waiting times to improve the effective performance of service and attention. This paper pioneers the developing and detailed study of a waiting time control chart for a retrial queue with general service times. Two waiting time control charts, the Shewhart control chart, and a control chart using the weighted variance method are constructed in this paper. We present three cases for the Shewhart control chart in which the service time obeys special distributions, such as exponential, Erlang, and hyper-exponential distributions. The case of an exponentially distributed service time is also presented for the control chart using the weighted variance method. Based on the numerical simulations conducted herein, managers can better monitor and analyze the customers’ waiting times for their service systems and take preventive measures.

Research on Automatic Unattended Bill Collection, Paste and Verifiction Integrated Robot Equipment and Control Platform based on Deep Convolutional Neural Network

A new solution for fully automated and unmanned ticket pasting verification based on deep convolutional neural networks is designed to address the issues of low efficiency, error-proneness, and wastage of manpower in the supplier service hall. The technology makes full use of machine vision and image processing, AI precise positioning correction algorithm and other methods to build an automatic unattended bill collection, paste and verification platform. Through the technologies of high-speed identification of invoice information, 3D vision-guidance planning, control of the path of robotic arm , detection of invoice pasting and repeating based on ultrasonic sensors, and tidal temporary storage of paper invoices, and so on, the automatic high-speed identification and inspection of bills in the supplier service hall are realized, and the efficiency and accuracy of bill processing in the supplier hall are improved. Experiments show that this research method reinforces ability of identification calibration and order correlation, and improves the efficiency of Invoice filing.

Flexible Sensor for Real-Time Monitoring of Motion Artifacts in Magnetic Resonance Imaging.

In recent years, magnetic resonance imaging has been widely used in the medical field. During the scan, if the human body moves, then there will be motion artifacts on the scan image, which will interfere with the diagnosis and only be found after the end of the scan sequence, resulting in a waste of manpower and resources. However, there is a lack of technology that halts scanning once motion artifacts arise. Here, we designed a real-time monitoring sensor (RMS) to dynamically perceive the movement of the human body and to pause in time when the movement exceeds a certain amplitude. The sensor has an array structure that can accurately sense the position of the human body in real time. The selection of the RMS ensures that there is no additional interference with the scanning results. Based on this design, the RMS can achieve the monitoring function of motion artifact generation.

SSD-Based Carton Packaging Quality Defect Detection System for the Logistics Supply Chain

Abstract With the deepening of green and sustainable development and the rapid development of the social economy, the modern logistics industry has also developed to an unprecedented level. In the logistics supply chain, due to the high value of the items inside the arrival carton, appearance inspection must be carried out before warehousing. However, manual inspection is slow and ineffective, resulting in the waste of manpower and packaging carton resources, which is not conducive to sustainable development. To address the above problems, this paper designs a logistics supply chain carton packaging quality defect detection system based on improved Single Shot MultiBox Detector (SSD) in the context of green sustainable development. The Implicit Feature Pyramid Network (IFPN) is introduced into SSD to improve the feature extraction ability of the model; the multiscale attention mechanism is introduced to collect more feature information. The experiment shows that the mAP and FPS of the system on the self-built data set reach 0.9662 and 36 respectively, which can realise the detection of the appearance defects of logistics cartons and help promote green sustainable development.

Pig face recognition based on improved YOLOv4 lightweight neural network

With the vigorous development of intelligence agriculture, the progress of automated large-scale and intensive pig farming has accelerated significantly. As a biological feature, the pig face has important research significance for precise breeding of pigs and traceability of health. In the management of live pigs, many managers adopt traditional methods, including color marking and RFID identification, but there will be problems such as off-label, mixed-label and waste of manpower. This work proposes a non-invasive way to study the identification of multiple individuals in pigs. The model was to first replace the original backbone network of YOLOv4 with MobileNet-v3, a popular lightweight network. Then depth-wise separable convolution was adopted in YOLOv4′s feature extraction network SPP and PANet to further reduce network parameters. Moreover, CBAM attention mechanism formed by the concatenation of CAM and SAM was added to PANet to ensure the network accuracy while reducing the model weight. The introduction of multi-attention mechanism selectively strengthened key areas of pig face and filtered out weak correlation features, so as to improve the overall model effect. Finally, an improved MobileNetv3-YOLOv4-PACNet (M-YOLOv4-C) network model was proposed to identify individual sows. The mAP were 98.15 %, the detection speed FPS were 106.3frames/s, and the model parameter size was only 44.74 MB, which can be well implanted into the small-volume pig house management sensors and applied to the pig management system in a lightweight, fast and accurate manner. This model will provide model support for subsequent pig behavior recognition and posture analysis.

Prediction of Nurses Allotment to Patient in Hospital through Game Theory

Allotment of nurses to patients is a critical task in terms of better treatment. Nurses should be appointed according to a patient's health condition, type of disease, and financial condition. Again, understaffing of nurses may hamper patient health and condition. Similarly, overstaffing of nurses is a waste of manpower. Adequate staffing of nurses is crucial. The authors propose a technique using game theory to meet overstaffing and understaffing of nurses. Game theory plays a vital role to meet the exact requirement. Nash equilibrium can be used for taking all possible decisions, like appointment of nurses in different categories for smooth treatment of patients. However, the final and most suitable decision can be taken using perfect Nash equilibrium. This technique is a perfect technique to implement in case of vital and critical decision points.

Design of Smart Energy Meter with IOT Using Arduino Mega

Electricity is one of the essential needs of human. It's commonly used for domestic, industrial, and agricultural purposes in day-to-day life. Most folks know the role of the energy meter within the electricity grid. It’s a fundamental component of the distribution grid. The energy meter helps the utility (electricity distribution company) to account for the utilization of electricity by the buyer on a KW per hour basis. To know this we want to seek out the drawbacks of the present energy meter, and therefore the biggest problem in electricity metering. The most important problem in electricity distribution is collecting meter reading data. Straight away meter reading is collected manually, which provides scope for corruption and human error in reading. It is the wastage of manpower and utility of resources. Our existing electricity meter isn't tamper-proof. There is to several temper cases are detected. In the existing meter, there is no under voltage and over voltage protection, as well as there is no over power use alert system. Because of these problems, the utility is unable to gather a fine for max demand cross. Power theft is the biggest problem in recent days, which causes a lot of loss to electricity boards. To overcome these drawbacks, the IoT-based Smart Energy Meter (SEM) is developed.

Monitoring Line Efficiency in Garment Industry using IoT

Abstract: The conventional method of monitoring efficiency in garment industry is to collect the number of pieces produced per hour by each line manually and entered in the website . This method is time consuming, wastage of man power, and involves non valued activities. The IOT based line efficiency monitoring system that checks the efficiency of end line production in garment industry in real time through weight sensor (one for each parameter: pass pieces, defect pieces) . Weight of the garment and the target per hour are given as the input. As a variation in the value of weight sensor signifies that garment is dropped into the bin, therefore the number of pieces produced is increased and vice versa. The Wi-Fi module in the system transfers data processed by the microcontroller, and transfers the data to the website as well as smart phone. This system can help the factory officials to monitor efficiency at real time basis. Keywords: IoT, Weight sensor, Node MCU, real time efficiency monitoring.

Prevention and Control of Spontaneous Combustion of Residual Coals in Acid‐Soaked Goaf in Gas Drainage Condition

With the increase of mine mining intensity, the scope of goaf continues to expand, a large amount of coal remains in goaf, and the spontaneous combustion fire of coal is particularly serious. In addition, the spontaneous combustion of coal under the condition of gas mining is not clear. At once, spontaneous combustion can induce gas explosion, resulting in the waste of manpower and material resources, bringing hidden dangers to the mine safety production. In order to prevent and control the fire caused by spontaneous combustion of left coal under the condition of gas mining, in this paper by means of laboratory test, field measurement, and numerical simulation, the law of spontaneous combustion of residual coal under gas mining is studied. The index gas of coal spontaneous combustion was determined by temperature‐programmed experimental device. Secondly, the distribution of the stope working face, goaf, and O2 is discussed. It is of great significance to study the spontaneous combustion rule of three belts in goaf under acid leaching condition and then to discuss the best position of injection idling opening and put forward targeted precontrol measures, which is of great significance to guarantee the safety of mine production. The results show that CO begins to appear when the temperature rises to 40 °C, and its concentration increases exponentially with the increase of coal temperature, which can reflect the degree of coal oxidation in goaf. Therefore, CO should be selected as the indicator gas in the working face. Under the influence of negative drainage pressure, the dangerous area will be enlarged. The optimal drainage volume of high drainage roadway is 90 m3/min, and the optimal air intake volume of working face is 1400 m3/min. The area 0–34 m away from the working face is scattered zone, 34–135 m away is spontaneous combustion zone, and 135 m away is asphyxiation zone. When the position of the idler is 50 m behind the working face, the reduction of the oxidation zone is the largest.

Prediction and optimization of performance and emission characteristics of a dual fuel engine using machine learning

The current research in engine, fuel and lubricant development are aiming towards environmental protection by reducing the harmful emissions. The testing under various conditions becomes mandatory before releasing product to meet the sustainable development goals of United Nations. This experimentation and testing under various operating conditions is time-consuming and tiresome process; it also leads to wastage of manpower, money, precious time and scarce resources. Intelligent techniques like Machine Learning (ML) has proven it's usage in almost all domains, trying to simulate the results as trained. This advantage is used to predict the performance and emission characteristics of a dual fuel engine. In this study, the experimental data are obtained from a single cylinder CI engine by operating under dual fuel mode using biogas and diesel as primary and secondary fuel respectively. The input parameters such as biogas flow rate, methane fraction (MF), torque and intake temperature are considered to predict the output parameters. The output parameters of the study includes performance attributes Brake thermal efficiency, secondary fuel energy ratio, and emissions attributes HC, CO, NOx and smoke. The proposed model uses Random forest Regressor and is trained using 324 distinct experiences recorded through physical experimentation. The model is validated using R2 score which is observed to be 0.997 for the given dataset while trained and tested in the ratio of 85:15. The outputs of the model are used to compute the output data for any new values of input attributes. The optimized values of the input parameters that could give maximum thermal efficiency and minimum emission is found using Lagrangian optimization. The optimized values are 12.48 Nm torque, 8.29 lit/min of biogas flow rate, methane fraction of 72.8%, intake temperature of 68.3 °C.

No-reference image quality assessment based on automatic machine learning

In different applications in deep learning, due to different required features, it is necessary to design specialized Neural Network structure. However, the design of the structure largely depends on the relevant subject knowledge of researchers and lots of experiments, resulting in huge waste of manpower. Therefore, in the field of Image Quality Assessment (IQA), the authors propose a method to apply Neural Architecture Search (NAS) to IQA. Mainly through the Differentiable Architecture Search algorithm, the structure of the modular Neural Network unit is searched by the stochastic gradient descent algorithm with better training performance by relaxing the operation features into a continuous space. Also, the idea of weight sharing is used to further save. The authors use the mainstream IQA database LIVE to search for Neural Network structures, and retrain and validate the searched structures in four datasets. A large number of experiments show that the model obtained by the search experiment achieves the effect of the best algorithm at this stage, and has a certain quality. The main contributions of this paper are: Transform the DARTS algorithm to adapt the regression problem, and introduce the Neural Architecture Search algorithm into the IQA field and conduct experimental verification.

DETERMINATION OF VARIANCE OF TIME TO RECRUITMENT WITH INTER-DECISION TIME AS GEOMETRIC PROCESS WHEN THE THRESHOLD HAS THREE COMPONENTS

In this paper, the problem of time to recruitment is analyzed for a single grade manpower system using an univariate CUM policy of recruitment. Assuming policy decisions and exits occur at different epochs, wastage of manpower due to exits form a sequence of independent and identically distributed exponential random variables, the inter-decision times form a geometric process and inter-exist time form an independent and identically distributed random variable. The breakdown threshold for the cumulative wastage of manpower in the system has three components which are independent exponential random variables. Employing a different probabilistic analysis, analytical results in closed form for system characteristics are derived

STOCHASTIC MODEL FOR TIME TO RECRUITMENT IN A SINGLE GRADE MANPOWER SYSTEM WITH TWO TYPES OF INTERDECISION TIMES WHEN THE BREAKDOWN THRESHOLD HAS THREE COMPONENTS

In this paper, the problem of time to recruitment is analyzed for a single grade manpower system in which attrition takes place due to two types of policy decisions where this classification is done according to intensity of attrition, it form an ordinary renewal process. Assuming (i) policy decisions and exits occur at different epochs (ii) wastage of manpower due to exits and wastage due to frequent breaks taken by the personnel working in the manpower system separately form a sequence of independent and identically distributed exponential random variables with different means and (iii) breakdown threshold for the cumulative wastage of manpower in the system has three components which are independent exponential random variables. A stochastic model is constructed and the variance of the time to recruitment is obtained using an univariate CUM policy of recruitment. Employing a different probabilistic analysis, analytical results in closed form for system characteristics are derived.

WQMS: water quality monitoring station for IoT

Water pollution threatens public health with infectious and transmissible diseases. The traditional approach of water quality monitoring consists in collecting samples of water and transporting them to specialised laboratories, which is a waste of manpower. Nowadays, Internet of Things (IoT) devices are capable of monitoring the water quality and reporting the data to a cloud server without any human intervention. However, related work does not present a comprehensive solution that covers the main design aspects; thus, we designed and built a water quality monitoring station (WQMS) that includes power management, data measurement, data transmission, and IoT. We deployed theWQMS in a remote fishpond with tilapias and measured the water quality for seven days in a row. The results show that the pH and dissolved oxygen can be expressed in terms of temperature as a quadratic function; besides, our solar energy harvesting module is a sustainable source of energy as long as there are no failures in the system.

Research on the Design and Application of Sports Competition Ticketing Platform Based on Edge Computing

Nowadays, under the encouragement of national and sports competition related policies, sports industry is gradually rising. In this case, how to provide ticketing services conveniently, quickly, and accurately for sports spectators all over the country and the world and how to keep up with the times and improve traditional ticketing services are two major problems that need to be solved urgently by those who are in charge of sports events. When investigating the current traditional ticketing platform for sports competitions, it is not difficult to find that most ticketing services rely heavily on manual services. From ticket purchase to ticket checking, consumers have poor experience, there is a lot of waste of manpower and material resources, and scalpers easily sell high‐priced tickets. In addition, the traditional ticket sales mode still uses old‐fashioned paper tickets or cards, which are difficult to preserve, making it difficult to prevent counterfeiting and use and control information statistics. Data confusion leads to financial risks. The “paralysis” of the ticketing system of many large‐scale sports competitions, especially super‐large sports competitions, just proves the importance and necessity of reforming ticketing platform of sports competitions in order to keep pace with development. This paper will study a large‐scale (or super‐large), flexible, efficient, and excellent performance, which can meet the burst peak access requirements of sports competition ticketing platform. We will first consider the overall demand of the sports competition ticketing market, then make a general analysis according to the demand, design a sports competition ticketing platform that can effectively serve large‐scale or super‐large sports competitions, and raise and solve the problems encountered when building the platform.

Smart Energy Monitoring using ARM Cortex

Nowadays, the demand for electricity increases due to an abrupt increase in population. In the existing system, the physical energy meter reading is taken, which is inefficient and is an outdated concept. This will require a large number of laborers. The person comes from the electricity board to the consumer's house for taking the readings from the energy meter and then, has to calculate the amount of energy consumption. This process is time-consuming and is a waste of manpower and will cause a lot of errors. A solution is, to implement a smart energy meter that will monitor energy consumption in terms of unit cost on an hourly and daily basis, in order to reduce human errors, physical labor,s and to improve the efficiency of the system. The proposed system will solve all the problems which are in existing energy meters. The proposed work calculates the energy consumption and sends the data to the consumer’s mobile via the global system for mobile communication (GSM) module, and the Bluetooth module HC-05 will also give them information on Android application, only when the Bluetooth is in the range of 100 meters from the smart meter. Both of these modules are connected to the ARM Cortex Board (AT91SAM3X8E). It has low power consumption and lower cost. The smart energy meter will also have a display unit that will show the real-time running data and consumed unit. When there is no supply from Maharashtra State Electricity Board (MSEB), then the smart meter will automatically switch to the inverter or battery backup through the relay. The proposed system is user friendly and has better efficiency than the existing energy meter.

Cost-benefit analysis on remote maintenance for industrial robot

The maintenance of industrial robots can be divided into two types: onsite maintenance and remote maintenance, each of which has its advantages and disadvantages. Firstly, the two types of maintenance modes, cost and benefit are analyzed and compared. While cost of on-site maintenance mainly includes downtime loss, maintenance cost and expert cost, downtime loss includes production loss and manpower waste, maintenance cost includes testing cost and material cost, and expert cost includes travel expense and labor cost. Cost of remote maintenance also includes downtime loss and maintenance cost, but does not include expert cost. Downtime loss and maintenance cost of remote maintenance is same as that of onsite maintenance, but the value is different. Then, cost-benefit method is used to analyze and establish cost-benefit model of two maintenance modes. Finally, an automobile manufacturing enterprise is taken as an example, and the cost and benefit of on-site maintenance and remote maintenance is analyzed and calculated to apply and verify the proposed method. The method proposed in this paper can provide help for the maintenance mode selection of industrial robots.

Research on Optimized Online Allocation of Scope Spatial Crowdsourcing Tasks

Task allocation of spatial crowdsourcing tasks is an important branch of crowdsourcing. Spatial crowdsourcing tasks not only require workers to complete a specific task at a specified time, but also require users to go to the designated location to complete the corresponding tasks. In this paper, Scope spatial crowdsourcing task whose work position is a region rather than a location is a kind of spatial crowdsourcing task. Mobile crowdsourced sensing (MCS) is one of the most important platforms to publish spatial crowdsourcing tasks, based on which MCS workers can use smartphones to complete the collections of related sensing data. When assigning tasks for scoped crowdsourcing tasks, there is a scope overlap between tasks and one or more tasks due to the association of task scope between tasks, which causes a waste of manpower. The focus of this paper is to study the redundancy of the task scope that occurs when using MCS to collect scoping data in the case of fewer workers and more tasks. Optimizing scope spatial crowdsourcing tasks allocation algorithm (OSSA) can eliminate the redundancy of the task area by integrating and decomposing tasks and achieve the improvement of the assignable number of tasks. In the Windows platform, experiments are made to compare the efficiency of the OSSA algorithm with the greedy algorithm and the two-phase-based global online allocation (TGOA) algorithm to further prove the correctness and feasibility of the algorithm for task scope optimization.

Evaluating IoT based passive water catchment monitoring system data acquisition and analysis

Water quality is the main aspect to determine the quality of aquatic systems. Poor water quality will pose a health risk for people and ecosystems. The old methods such as collecting samples of water manually and testing and analysing at lab will cause the time consuming, wastage of man power and not economical. A system is needed to provide a real-time data for environmental protection and tracking pollution sources. This paper aims to describe on how to monitor water quality continuously through IoT platform. Water Quality Catchment Monitoring System was introduced to check and monitor water quality continuously. It’s features five sensors which are temperature sensor, light intensity sensor, pH sensor, GPS tracker and Inertia Movement Unit (IMU). IMU is a new feature in the system where the direction of x and y is determined for planning and find out where a water quality problem exists by determining the flow of water. The system uses an internet wireless connection using the ESP8266 Wi-Fi Shield Module as a connection between Arduino Mega2560 and laptop. ThingSpeak application acts as an IoT platform used for real-time data monitoring.