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This study aims to apply for the first time in literature a new multi-objective sensor selection and placement optimization methodology based on the multi-objective Lichtenberg algorithm and test the sensors' configuration found in a delamination identification case study.

This work aims to study the damage identification in an aircraft wing using the Lichtenberg and multi-objective Lichtenberg algorithms. The former is used to identify damages, while the last is associated with feature selection techniques to perform the first sensor placement optimization (SPO) methodology with variable sensor number. It is applied aiming for the largest amount of information about using the most used modal metrics in the literature and the smallest sensor number at the same time.

The proposed method was not only able to find a sensor configuration for each sensor number and modal metric but also found one that had full accuracy in identifying delamination location and severity considering triaxial modal displacements and minimal sensor number for all wing sections.

This study demonstrates for the first time in the literature how the most used modal metrics vary with the sensor number for an aircraft wing using a new multi-objective sensor selection and placement optimization methodology based on the multi-objective Lichtenberg algorithm.

This paper seeks to present an integrated principal component analysis (PCA) data envelopment analysis (DEA) framework for assessment and ranking of manufacturing systems based on equipment performance indicators.

The integrated framework discussed in this paper is based on PCA and DEA. The validity of the integrated model is further verified and validated by numerical taxonomy (NT) methods.

The results of the integrated PCA DEA framework show the ranking of sectors and weak and strong points of each sector with regard to equipment and machinery. Moreover, a non‐parametric correlation method, namely, Spearman correlation experiment shows high level of correlation among the findings of PCA, DEA and NT. Furthermore, it identifies which indicators have major impacts on the performance of manufacturing sectors.

To achieve the objectives of this study, a comprehensive study was conducted to locate all economic and technical indicators which influence equipment performance. These indicators are related to equipment productivity, efficiency, effectiveness and profitability. Standard factors such as down time, time to repair, mean time between failure, operating time, value added and production value were considered as shaping factors. The manufacturing sectors are selected according to the format of International Standard for Industrial Classification.

The modeling approach of this paper could be used for ranking and analysis of other sectors in particular or countries in general.

Large supermarkets, chain stores and enterprises with large-scale warehousing put forward higher standards and requirements for the automation and informatization of warehouses. As one of the fast-growing commercial supermarkets in China, the traditional warehouse management mode has restricted the rapid development of Yonghui Superstores to a certain extent. The purpose of this paper is to find out how the existing warehouse mode can be changed and to solve the existing problems of warehouse management of Yonghui Superstores.

This research puts forward construction of warehouse center, which is based on radio frequency identification (RFID) and sensor technology, then designs the model for receiving, storage, operations management, distribution and outbound to solve the existing problems of warehouse management of Yonghui Superstores.

What technologies should be adopted to meet storage requirements? How to monitor the storage environment in real time and improve the operation and management level of the warehouse? This study found that building a warehouse center based on RFID and sensor technology was a good solution.

The Yonghui Superstores warehouse center model lacks corresponding simulation experiments, and the investment and income are difficult to estimate quantitatively.

This paper has designed and discussed the warehouse center model based on RFID and sensor technology, which provides a few references for the actual investment and construction of a warehouse center. In addition, the warehouse center model has strong generalized applicability and could be widely used in various enterprises.

The warehouse center could improve the warehouse management level of Yonghui Superstores and change the traditional warehouse management mode. To some extent, it improves the enterprise flexibility of the market, which will be of great significance to improve business efficiency and enhance brand image and competitiveness.

This study takes Yonghui Superstores as a case to analyze the problems of warehousing management in detail and then designs a warehouse center based on RFID and sensor technology. The study discusses the location and distribution, software and hardware selection, benefits evaluation, significances and return on investment, which makes the warehouse center model versatile, technically feasible and economically applicable.

The purpose of this paper is to present the development of a Matlab/Simulink‐based simulation environment for the design and testing of indirect and direct adaptive flight control laws with fault tolerant capabilities to deal with the occurrence of actuator and sensor failures.

The simulation environment features a modular architecture and a detailed graphical user interface for simulation scenario set‐up. Indirect adaptive flight control laws are implemented based on an optimal control design and frequency domain‐based online parameter estimation. Direct adaptive flight control laws consist of non‐linear dynamic inversion performed at a reference nominal flight condition augmented with artificial neural networks (NNs) to compensate for inversion errors and abnormal flight conditions following the occurrence of actuator or sensor failures. Failure detection, identification, and accommodation schemes relying on neural estimators are developed and implemented.

The simulation environment provides a valuable platform for the evaluation and validation of fault‐tolerant flight control laws.

The modularity of the simulation package allows rapid reconfiguration of control laws, aircraft model, and detection schemes. This flexibility allows the investigation of various design issues such as: the selection of control laws architecture (including the type of the neural augmentation), the tuning of NN parameters, the selection of parameter identification techniques, the effects of anti‐control saturation techniques, the selection and the tuning of the control allocation scheme, as well as the selection and tuning of the failure detection and identification schemes.

The novelty of this research efforts resides in the development and the integration of a comprehensive simulation environment allowing a very detailed validation of a number of control laws for the purpose of verifying the performance of actuator and sensor failure detection, identification, and accommodation schemes.

Internet has changed radically in the way people interact in the virtual world, in their careers or social relationships. IoT technology has added a new vision to this process by enabling connections between smart objects and humans, and also between smart objects themselves, which leads to anything, anytime, anywhere, and any media communications. IoT allows objects to physically see, hear, think, and perform tasks by making them talk to each other, share information and coordinate decisions. To enable the vision of IoT, it utilizes technologies such as ubiquitous computing, context awareness, RFID, WSN, embedded devices, CPS, communication technologies, and internet protocols. IoT is considered to be the future internet, which is significantly different from the Internet we use today. The purpose of this paper is to provide up-to-date literature on trends of IoT research which is driven by the need for convergence of several interdisciplinary technologies and new applications.

A comprehensive IoT literature review has been performed in this paper as a survey. The survey starts by providing an overview of IoT concepts, visions and evolutions. IoT architectures are also explored. Then, the most important components of IoT are discussed including a thorough discussion of IoT operating systems such as Tiny OS, Contiki OS, FreeRTOS, and RIOT. A review of IoT applications is also presented in this paper and finally, IoT challenges that can be recently encountered by researchers are introduced.

Studies of IoT literature and projects show the disproportionate importance of technology in IoT projects, which are often driven by technological interventions rather than innovation in the business model. There are a number of serious concerns about the dangers of IoT growth, particularly in the areas of privacy and security; hence, industry and government began addressing these concerns. At the end, what makes IoT exciting is that we do not yet know the exact use cases which would have the ability to significantly influence our lives.

This survey provides a comprehensive literature review on IoT techniques, operating systems and trends.

The presence of ferrous wear debris in lubricating oil may cause progressive damage in the internal combustion engines. Online monitoring of the size and concentration of these particles in the oil is a way to optimize the engine performance and its life cycle.

In this study, an online sensor was designed and fabricated to identify ferrous wear particles in the engine oil based on the induction method. The diameter of the sensor outlet duct was designed as small as possible to generate a high-intensity magnetic induction and achieve a proper sensitivity in the sensor. The experiments were designed and performed in offline mode. Furthermore, to evaluate the actual performance of the sensor in presence of iron particles in the oil, online tests were performed at different sizes and concentrations.

It was concluded from offline tests that the highest sensitivity of the sensor occurs at the frequency and voltage of 2.5 kHz and 120 V, respectively. According to the results of the online tests, the larger the particle size, the higher the peaks at the sensor output. Also, a high density of the peaks was observed in the sensor output graphs as the concentration of particles was increased.

The proposed sensor was able to identify ferrous wear particles larger than 125 µm separately, which is the failure limit in the internal combustion engines.

The electronic nose is an array of chemical or gas sensors and associated with a pattern-recognition framework competent in identifying and classifying odorant or non-odorant and simple or complex gases. Despite more than 30 years of research, the robust e-nose device is still limited. Most of the challenges towards reliable e-nose devices are associated with the non-stationary environment and non-stationary sensor behaviour. Data distribution of sensor array response evolves with time, referred to as non-stationarity. The purpose of this paper is to provide a comprehensive introduction to challenges related to non-stationarity in e-nose design and to review the existing literature from an application, system and algorithm perspective to provide an integrated and practical view.

The authors discuss the non-stationary data in general and the challenges related to the non-stationarity environment in e-nose design or non-stationary sensor behaviour. The challenges are categorised and discussed with the perspective of learning with data obtained from the sensor systems. Later, the e-nose technology is reviewed with the system, application and algorithmic point of view to discuss the current status.

The discussed challenges in e-nose design will be beneficial for researchers, as well as practitioners as it presents a comprehensive view on multiple aspects of non-stationary learning, system, algorithms and applications for e-nose. The paper presents a review of the pattern-recognition techniques, public data sets that are commonly referred to as olfactory research. Generic techniques for learning in the non-stationary environment are also presented. The authors discuss the future direction of research and major open problems related to handling non-stationarity in e-nose design.

The authors first time review the existing literature related to learning with e-nose in a non-stationary environment and existing generic pattern-recognition algorithms for learning in the non-stationary environment to bridge the gap between these two. The authors also present details of publicly available sensor array data sets, which will benefit the upcoming researchers in this field. The authors further emphasise several open problems and future directions, which should be considered to provide efficient solutions that can handle non-stationarity to make e-nose the next everyday device.

An artificial immune system (AIS) for the detection and identification of abnormal operational conditions affecting an unmanned air vehicle (UAV) is developed using the partition of the universe approach. The performance of the proposed methodology is assessed through simulation within the West Virginia University (WVU) unmanned aerial system (UAS) simulation environment.

An AIS is designed and generated for a fixed wing UAV using data from the WVU UAS simulator. A novel partition of the universe approach augmented with the hierarchical multiself strategy is used to define the self, within the AIS paradigm. Several 2-dimensional and 3-dimensional commanded trajectories are simulated under normal and abnormal conditions affecting actuators and sensors. Data recorded are used to build the AIS and develop an abnormal condition detection and identification scheme for the two categories of subsystems. The performance of the methodology is evaluated in terms of detection and identification rates, false alarms and decision times.

The proposed methodology for UAV abnormal condition detection and identification has the potential to support a comprehensive and integrated solution to the problem of aircraft subsystem health management. The novel partition of the universe approach has been proven to be a promising alternative to the previously investigated clustering methods by providing similar or better performance for the cases investigated.

The promising results obtained within this research effort motivate further investigation and extension of the proposed methodology toward a complete system health management process, including abnormal condition evaluation and accommodation.

The use of the partition of the universe approach for AIS generation may potentially represent a valuable alternative to current clustering methods within the AIS paradigm. It can facilitate a simpler and faster implementation of abnormal condition detection and identification schemes.

In this paper, a novel method for AIS generation, the partition of the universe approach, is formulated and applied for the first time for the development of abnormal condition detection and identification schemes for UAVs. This approach is computationally less expensive and mitigates some of the issues related to the typical clustering approaches. The implementation of the proposed approach can potentially enhance the robustness of UAS for safety purposes.

The lack of integrity of the piston machine combustion chamber manifests itself in leakages of the working fluid between the piston and the cylinder liner, at valves mounted in the cylinder head and between the head and the liner. An untight combustion chamber leads to decreased power output or efficiency of the engine, while leaks of a fluid may cause damage to many components of the chamber. The actual value of working chamber leak is a desired and essential piece of information for planning operations of a given machine.

This research paper describes causes and mechanisms of leakage from the working chamber of internal combustion engines. Besides, the paper outlines presently used methods and means of leak identification and states that their further development and improvements are needed. New methods and their applicability are presented.

The methods of leak identification have been divided into diagnostic and non-working machine leak identification methods. The need has been justified for the identification of leakage from the combustion chamber of a non-working machine and for using the leakage measure as the value of the cross-sectional area of the equivalent leak, defined as the sum of cross-section areas of all leaking paths. The analysis of possible developments of tightness assessment methods referring to the combustion chamber of a non-working machine consisted in modelling subsequent combustion chamber leaks as gas-filled tank leak, leak from another element of gas-filled tank and as a regulator of gas flow through a nozzle.

A measurement system was built allowing the measurement of pressure drop in a tank with the connected engine combustion chamber, which indicated the usefulness of the system for leakage measurement in units as defined in applicable standards. A pneumatic sensor was built for measuring the cross-sectional area of the equivalent leak of the combustion chamber connected to the sensor where the chamber functioned as a regulator of gas flow through the sensor nozzle. It has been shown that the sensor can be calibrated by means of reference leaks implemented as nozzles of specific diameters and lengths. The schematic diagram of a system for measuring the combustion chamber leakage and a diagram of a sensor for measuring the cross-sectional area of the equivalent leak of the combustion chamber leakage are presented. The results are given of tightness tests of a small one-cylinder combustion engine conducted by means of the set up measurement system and a pre-prototype pneumatic sensor. The two solutions proved to be practically useful.

The purpose of this paper was to present the process of building hardware and software for a collision avoidance system of a quadrotor capable of an indoor autonomous flight.

The system development was carried out in two steps. First, the quadrotor system was designed to mount mission equipments for an indoor flight. The prediction error minimization (PEM) method was used for system identification of the quadrotor, and the linear quadratic regulator (LQR) control method was used for the attitude control. Second, a collision detection system was realized by using a Kinect sensor, an embedded board and a ground control system (GCS). A Kinect sensor with embedded board can send the 3D depth information to GCS and then the GCS displays the 3D depth information with a warning message.

As the controller design requires a linear model, the PEM method was used in system identification. The LQR was used in controller design. It was found that the use of the PEM method for system identification was effective for developing a linear model required for a practical control system using LQR. As 3D depth information from a Kinect sensor is quite accurate in an indoor environment, a collision detection system with Kinect was successfully developed.

The step-by-step approach presented in this paper can be used to develop an autonomous aerial vehicle capable of navigating in an indoor environment with obstacles.

The primary contribution of the paper is the presentation of a practical method for developing a low-cost collision avoidance system for a quadrotor vehicle.