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Free-space detection is considered as one of the most important tasks in intelligent vehicle, which remains the essential requirement for obstacle detection and path planning. As a part of Internet of Things, vehicle-based device produces a large amount of data requiring to be processed in realtime. Therefore, the present study proposes a fast free-space detection system with a mobile platform based stereo vision. The current system processes the image-based perception signal in terminal equipment, which avoids lots of data transmissions between cloud and vehicle. In terms of flexibility and robustness, a fast spare disparity matching algorithm is suggested to be real-timely processed in a low-power mobile platform. Based on the spare disparity map, the current work improves the u-disparity space algorithm as the u-log-disparity space with the aim to detect the free-space for significantly lowering the running time. Besides, we also provide a complete system design including hardware and software. The experiments demonstrate that our system is precise and robust at complex real-time driving environment.

Due to the limited computing capacity in mobile devices, device-to-device (D2D) computation offloading has been proposed as a promising solution to improving the quality of service in the Internet of things (IoT) networks, by allowing mobile devices to exploit spare computing resources in nearby user devices. However, a major challenge to realizing this new paradigm is how to effectively motivate user devices to participate as computation providers (CPs) for computation requesters (CRs), which is further exacerbated by the fact that user incentives are usually coupled with information asymmetry between the network operator and user devices. This has not been sufficiently studied for D2D computation offloading. In this paper, we propose a signaling-based incentive mechanism that leverages contract theory to address information asymmetry for D2D computation offloading. Based on the proposed contract-based incentive mechanism, we also solve the many-to-many CP-CR pairing problem by devising a polynomial-complexity matching scheme. Simulation results show that our proposed algorithm can effectively motivate user devices to participate in D2D computation offloading and select the most appropriate CPs to perform the computation tasks for corresponding CRs.

Reliability is increasingly a major concern in network-on-a-chip (NoC) design, alongside increased performance demands from new applications and the need for continued miniaturization of silicon technology. In this article, we look at the task migration mechanism, used to recover from permanent processing element (PE) failures in NoCs, by remapping tasks performed on faulty cores to spare ones. An innovative reliability-aware task mapping technique is presented, based on a hybridization between Multi-Objective Optimization (MOO) and Reinforcement Learning (RL). It takes place in two steps. In the first, a set of optimal remapping solutions for different failure scenarios is generated at design-time, using a Biogeography-Based Multi-Objective Optimization algorithm, while considering communication energy and migration costs. In the second step, an artificial neural network agent is trained to select the best remapping solution, from those generated at design-time, to recover from execution failures at run-time. Experiments were carried out to evaluate our technique for different sizes of networks and on different benchmarks. The results obtained show that the technique based on the hybridization MOO_RL brings a great improvement in the reliability of the NoC and achieves a good compromise between reliability and performance. It also guarantees a reduction of the overhead caused by the storage space of the remapping solutions, compared to the existing solutions.

The paper presents a method for determining maintenance and spare provisioning costs, for systems containing a non exponential component. The input data of the model are R ( t ) - the reliability function with time of the component, T the duration of the life cycle, cost of a repair, cost of a spare part and the turnaround time. The model is applicable for any form of R ( t ) : Weibull, Normal, expressed by an explicit or implicit set of equations and even in the form of discrete frequency table. The calculations can be performed by a simple Excel worksheet and do not require expensive tools (e.g. a flexible Monte Carlo package): The algorithm requires a few iterations of the dot product of two matrices. The entries of the matrices are calculated from the input values and the results of previous iterations, at M equidistant time points along the life cycle. M determines the accuracy of the calculations. The paper indicates sufficient conditions for M , which ensure a desired accuracy. Numerical examples performed on an Excel worksheet are presented. Since the model is applicable for any R ( t ) , it was applied, for verification purposes for R ( t ) = e − λ t , which has well known accurate results. The comparison confirmed the accuracy of the approximations.

For real-time computing systems, energy efficiency, Quality of Service, and fault tolerance are among the major design concerns. In this work, we study the problem of reliable and energy-aware (m,k)-deadlines enforcement using standby-sparing under fixed-priority assignment. The standby-sparing systems adopt a primary processor and a spare processor to provide fault tolerance for both permanent and transient faults. In order to reduce energy consumption for such kind of systems, we proposed two novel scheduling schemes under the QoS constraint of (m,k)-deadlines: one for task sets partitioned with deeply-red pattern and one for task sets partitioned with evenly distributed pattern. The evaluation results demonstrate that our proposed approaches significantly outperformed the previous research in energy conservation while assuring (m,k)-deadlines and fault tolerance for real-time systems.

As one of the critical issues in operations management, spare parts planning classification systems based only on cost may not withstand the test of time because of continuing technological advancement or environmental degradation. This study, however, emphasizes using failure mode effect and criticality analysis (FMECA) as a basis for designing an ABC classification system that is capable of more accurately determining critical equipment and making maintenance more efficient. In the proposed methodology, risk priority numbers (RPN) and criticality as an index of safety and reliability can be obtained using a structured failure analysis technique. These indexes can be added to the economic indicator of maintenance cost, and then a classification model of spare parts can be established according to a comprehensive scoring method. The model is calibrated by using case data from a bus braking system, and the components of the braking system are compared and analyzed. The results show that the improved ABC classification method reduces the proportion of key and main components. This method can make maintenance work more efficient, targeting the most critical components, and can reduce administrative costs for enterprises.

Cellular Base Stations (BSs) are equipped with backup batteries. These batteries have some spare capacity over time while maintaining the power supply reliability, so they are potential flexible resources for power systems. This letter exhibits the insight to explore the BS dispatch potential towards power system frequency regulation. For each BS, the feasible dispatch boundaries of participating in frequency regulation are estimated. Then a framework is proposed to coordinate BSs to provide frequency support. By incorporating massive distributed BSs, the power system frequency performances can be improved.

Industrial assets exposed to random environment often exhibit complex deterioration mechanisms with health status variations. In actual field operation, hidden defect signals are usually crucial indicators of upcoming malfunctions and also reminders of proactive maintenance executions. Despite the extensive applications of defect-centered maintenance, in the literature, little attempt has: a) captured the impact of random environments on health variation and restoration, and b) explored the differentiated functions of maintenance windows in separate states. This article addresses these challenges by introducing a state-based maintenance policy with multifunctional maintenance windows. The impact of environmental disturbance on both defect initialization and propagation is characterized by random increment of the state transition rate as well as probabilistic malfunction risk. Three types of maintenance windows (regular, opportunistic, and postponed) are scheduled to ensure a flexible scheduling of inspection and spare part resources. Importantly, the function of opportunistic window is state-based, defect identification when normal and removal when defective. The objective is to minimize the cost rate via the joint optimization of inspection interval, postponed interval, and opportunistic threshold. Experimental studies demonstrate the superior performance of this policy over some conventional policies.

Modern engineering systems are usually equipped with a variety of sensors to measure real-time operating conditions. Based on the condition monitoring data from multiple sensor sources, this paper deals with a dynamic predictive maintenance scheduling using deep learning ensemble for system health prognostics. The deep learning ensemble model is composed of deep autoencoder and bidirectional long short-term memory in series, and aims to accurately estimate the system health state and remaining useful life. The deep autoencoder is used to extract the deep representative features hidden in condition monitoring data, whereas the inclusion of the bidirectional long short-term memory allows learning the temporal correlation information of features in both forward and backward time directions. Thus, the combination of the two models forms an effective model. With obtained prognostic information, optimal maintenance decisions are determined by two designed rules. The first rule presets a reliable remaining useful life threshold to dynamically judge whether maintenance is carried out at current inspection time, while the second one selects a degradation state to dynamically determine whether to place a spare part order. The performance of the proposed dynamic predictive maintenance strategy is measured by the maintenance cost per unit operating time (cost rate) using the aero-engine dataset from NASA. Its effectiveness is demonstrated by comparing with recent publications.

Employees have a very important role, where they must spare their energy and thoughts to achieve company goals. The management of corporate culture is directed to the ability to encourage company performance through improving employee performance. The purposes of this study are to examine and analyze the effect of organizational culture and competence on organizational commitment, employee performance, and employee performance. The population and the respondents were 76 people in the Karangasem Regency Government. This study used the SEM analysis with SmartPLS 3.0 program. The findings indicate that organizational culture has a positive effect on organizational commitment and employee performance. Competence affects both the organization's commitment and employee performance positively. Organizational commitment also affects the performance of employees positively and and mediate the relationship between organizational culture and competence on employee performance.

Inventory ensures stability of the production technical system. Production facilities have warehouses for workpieces to create final output parts, and warehouses for finished products to meet customer demand. Stocks of workpieces avoid manufacturing downtime. Stocks of finished products make it possible to meet demand in a timely manner and avoid penalties for late deliveries of finished products to consumers.On the other hand, the objects that make up the inventory are «dead» material resources that do not bring profit and negatively affect economic efficiency and competitiveness. According to published data, in economically developed countries, such as the USA, Japan, Germany and others, up to 30–40 % of production is in stocks. All this indicates the relevance of the issue of optimising the level of reserves, of solving the problem of establishing and maintaining the optimal level of the stock, its timely replenishment, considering time required for execution of applications of end users.The objective of the workis to propose a solution to a stochastic logistic problem of inventory management with a random time delay and random demand, obtained by the authors using the methods of mathematical statistics, mathematical modelling, and production logistics for unknown and arbitrary form of distribution functions of demand and time delay. The work provides specific examples of approaches to implementation of suggested solution.

This paper focuses on a novel model named multi-station fusion (MSF). The proposed model integrates transformer substation, data center, energy storage system (ESS), photovoltaic (PV), electric vehicle charging station (EVCS), connection information base station and other systems. It aims at improving the utilization rate of the spare space in existing substation, reducing operation and maintenance cost, and increasing the revenue of power grid enterprises. This paper designs three types of AC/DC system based on reliability priority, economy priority and retrofit flexibility priority. Markov process Monte Carlo method is introduced to verify data center reliability in MSF. The business model of MSF is analyzed. Finally, the construction scheme of MSF model, the contents of the planning considerations and cost–benefit study demonstrate the economic efficiency of the proposed model.

The development of the automotive industry at this time can be quite rapid because every year the number of vehicles continues to increase. The development of the automotive industry also encourages the development of industries supporting services such as car repair shops, car salons, sales of spare parts, the sale of accessories, and car wash services. Own car wash services are needed the car users because, in addition to saving time, the car users are also convinced that if their car is washed at the car wash, the car will be clean and well maintained. In offering their services, employers must implement appropriate promotional strategies. Quality of service also supports the company to provide satisfaction for its customers. The purpose of this study is to determine the effect of promotion strategies and quality of service on user satisfaction at Best Car Care Bogor partially or simultaneously. A population of 220 people and take a sample of 135 respondents. The research method using multiple regression analysis. Results of research conducted found that the promotion strategy and service quality and significant positive effect either simultaneously or partially on user satisfaction. Advice for employers Best Car Care Bogorfor more attention to the satisfaction of the users of services Best Car Care Bogor, to increase the use of services

Currently, with the growth of several technologies in the manufacturing processes (e.g. sintering, hybrid milling), some discussions in the construction of spare-parts have increased, such as manufacturing time, costs and initial investment for the implementation of the system. Taking into account the complexity and diversity of car parts and models, the automobile industry is an inexhaustible source of studies when talking about spare-parts, since maintaining molds for casting and cataloging everything since the invention of the automobile it would be impossible. Thus, using the concept of industry 4.0 and technologies in the area of manufacturing processes, this study aims to evaluate and describe comparatively the conventional and new manufacturing processes.

Benneta Motor Workshop is one of the individual businesses that was founded in 2017, engaged in the business of selling motorcycle spare parts and servicing services. In running its business, Benneta workshop still uses conventional systems so that there are several obstacles found starting from the data management process, purchase transactions, sales and service services, to the process of making reports which are still recorded manually in notes and stored in a ledger so that it is still less efficient. in terms of time and process. Conventional systems cause the data to be inaccurate and not fast in recording so it takes a long time to do it. The purpose of this research is to build an information system for selling spare parts and motorcycle service using Visual Basic.Net programming language and MySQL database. In this study, the authors used a qualitative descriptive research method. The design method used in this application uses the waterfall method and the testing method used by the author is blackbox testing. With the creation of this system, it is hoped that it will provide efficiency and work effectiveness at the Benneta Motor Workshop.

THEPURPOSE.To consider the features of development and exploitation of DC-DC power supplies for Arctic conditions, to show the key problems and existing methods of solving them, to identify prospective directions in the design of this type of devices.METODS.Theworkismainlyanover-viewwiththequestionsofconstruction,circuitdesign,controlprinciples,maintainingthermalequilibrium,choosingofelectricelementsandminiaturization.Thefirstpartofthearticleisdevotedtothereviewofscientificpublicationsandpatents,in the second part the idea of using a parallel architecture in frost-resistant power supplies is considered, giving an opportunity to adapt modular principle of device construction.RESULTS.Thearticleexaminestheissuesofadaptingthemodularapproachtothespecificsoffrost-resistantpowersupplies.Inthiswayyoucanbuilda self-healing, configurable, easy-to-repair power supply of wide application. With modular construction principle the entire electrical complex consists of parallel-connected cells, placed in one hermetic and heated case (a case with connectors on the surface, as well as the control and display units).CONCLUSION. Parallelconnectionofthecellsallowstoprovidea significant power reserve in the power supply, to perform a quick replacement of faulty modules without interrupting the power supply process, to increase/reduce the system power easily by changing the number of working cells. The system acquires special advantages, if the output voltage levels of the cells are adjustable. In this case, it is possible to build a universal configurable power supply of wide application,whichisa novelty on the market. If you group the cells on the output, you can use one power supply for several different consumers. The spare parts for the power supply are as simple as possible and consists of ready-made cells and connecting wires. The obtained results can be used in development of fault-tolerant secondary DC-DC power supplies for hard operating conditions.

Los elementos mecánicos con pares tribológicos tales como las bombas de agua de uso industrial tipo centrífuga son equipos de primera línea de trabajo en las industrias de consumo alimenticios, en la actualidad la población ha crecido drásticamente por tal razón las industrias tratan de optimizar las operatividades de estos equipos que contienen pares tribológicos debido a las series de desgastes que estos presentan al momento de estar en función y más aún si carecen de un mantenimiento adecuado y periódico. Mantenimiento es un término muy empleado en el día a día de las personas y de forma genérica y amplia. Parece una tarea difícil definir el mantenimiento desde el punto de vista industrial. La mayoría de las personas entiende por mantenimiento el acto de arreglar y reparar equipos e instalaciones para asegurar la continuidad operativa, es decir, conservación y no mantenimiento, pero, además, es mantener, prevenir, analizar, mejorar continuamente, dominar tecnológicamente y económicamente los medios productivos con seguridad y preservación del medio ambiente, el mantenimiento debe iniciarse bajo un control estricto de medidas técnicas y direccionadas en la condición de cada activo, llevando acabo una definición de la política de mantenimiento, organización de la documentación, registro y suministro de repuestos y procedimientos adecuados. En este proyecto se diagnosticarán los fallos funcionales en las bombas de agua tipo centrífugas y equipos de bombeos en general, estos son los excesivos rozamientos que se presentan en las zonas habituales tales como: ejes, rodamientos, sellos, etc. Palabras clave: Fricción, desgaste, lubricación, Tribología, Cavitación, Estectocoipio, viscosímetro, termómetro.

In this paper, a decision-making framework for the design of redistributed manufacturing (RdM) networks is developed. Redistributed manufacturing, a manufacturing paradigm greatly empowered by the Industry 4.0 toolset, is the shift in production towards geographically dispersed interconnected facilities. The framework is context independent, accounts for the collective impact of all decision-making levels on one another in an iterative manner, and incorporates uncertainty. The framework has been applied to a case study in the aerospace spare parts production sector. Results indicated that the RdM paradigm demonstrated considerable improvements in service level when compared with a traditional centralized counterpart, while it was not as competitive with regards to total cost. This paper contributes to the literature on model-based distributed manufacturing systems design under uncertainty, and enables informed decision-making regarding the redistribution of resources and decentralization of decision-making. The novelty of this paper is the approach employed to handle complexity, nonlinear interrelationships and uncertainty, within the domain of RdM network design. These computationally demanding attributes are handled through simulation, and only their impact is passed back to an analytical model that generates the RdM network.

System requirements in the automotive industry are constantly evolving and being improved. Currently, the entire supply chain, related to the automotive industry, is subject to the fourth amendment to the technical automotive standard - IATF 16949: 2016 Quality management systems - Detailed requirements for the use of ISO 9001: 2015 in serial production and production of spare parts in the automotive industry. In IATF standard, there are advices called reference manuals that provide important guidance on the quality system in this industry - one of them is the FMEA manual. The new guidelines in this regard, developed jointly by AIAG and VDA, entered into force in June 2019. Presentation of the most important changes introduced, as well as presentation of a practical example of the conducted analysis is the purpose of the article.

With low voltage (LV) distribution networks increasingly being re-purposed beyond their original design specifications to accommodate low carbon technologies, the ability to accurately calculate their actual spare capacity is critical. Traditionally, within the Great Britain (GB) power system, there has been limited monitoring of LV distribution networks, making this difficult. This paper proposes a method for estimating spare capacity of unmonitored LV networks using demand data from customer Smart Meters. In particular, the proposed method infers existing LV network capacity, as well as losses, across scenarios where only a limited number of customers have Smart Meters installed. Typical daily load profiles across customers with Smart Meters are learned using a Dirichlet sampled Gaussian mixture model (GMM). Learned profiles are then applied to all unmetered customers to estimate network parameters. Method accuracy is assessed by comparing estimations with simulated, fully observed, LV network models. The method is also compared to benchmark models for establishing unobserved demand profiles. Overall, results in the paper show that the proposed method outperforms benchmark models in terms of accurately assessing substation headroom, particularly in scenarios where only 10-50% of customers have Smart Meters installed.