Complex Operational Environments Research Articles

ВПЛИВ ФІНАНСОВОЇ КРИЗИ В УКРАЇНІ 2014—2015 РОКІВ НА РІВЕНЬ ФІНАНСОВОЇ БЕЗПЕКИ БАНКІВ

Визначено, що комерційні банки функціонують у складному операційному середовищі, а це зумовлено впливом низки факторів політичних, соціальних, економічних, що здебільшого є малопрогнозованими і деструктивними. Обґрунтовано зовнішні ключові параметри, через які опосередковується вплив на процес забезпечення фінансової безпеки банків. Досліджено взаємозв’язок між розміром ставки нормативів обов’язкового резервування і рівнем фінансової безпеки банку. Проаналізовано стан активів, зобов’язань і власного капіталу за групами банків упродовж 2012—2016 рр.. Визначено роль центрального банку в регулюванні діяльності банків щодо забезпечення їхньої фінансової безпеки. Обґрунтовано доцільність запровадження НБУ санкцій на проведення будь-яких фінансових операцій банків із російським капіталом на користь материнських структур. Визначено ключові проблеми, які стримують процес забезпечення фінансової безпеки банків у розрізі і функціональних складових (капітало-ресурсної безпеки, кредитно-інвестиційної, валютної та безпечного рівня доходів і витрат. Запропоновано методичний підхід до рейтингової оцінки банків за рівнем їхньої фінансової безпеки на основі аналізу показників-стимуляторів / дестимуляторів аналізованих банків. Обґрунтовано проведення рейтингової оцінки банку за рівнем його фінансової безпеки на основі порівняння j-го КБ з «еталоном» — банком, який має найкращі результати щодо кожного показника — максимальні значення для стимуляторів і мінімальні — для дестимуляторів. Рейтинг визначено як середнє арифметичне стандартизованих показників, зведених у матрицю ключових показників. Доведено необхідність групування аналізованих банків за рівнем їхньої фінансової безпеки. Обґрунтовано використання методу групування об’єктів дослідження як інструменту упорядкування аналізованих банків. Визначено емпіричним шляхом з обліком якісних, неформалізованих характеристик чотири групи банків за рівнем їхньої фінансової безпеки (з високим рівнем фінансової безпеки, достатнім, низьким і критичним).

Particle Filter-Based Inter-System Positioning Model for Non-Overlapping Frequency Code Division Multiple Access Systems

In the process of composing a double-differenced positioning model, it is difficult to separate different frequency signals between code division multiple access (CDMA) systems, the single-difference ambiguity of the pivot satellite and phase differential inter-system biases (PDISBs). Hence it is difficult to calibrate in advance the bias between systems in order to build an inter-system model which only needs one pivot satellite. Based on analysis of the stability of PDISB parameters for non-overlapping frequency CDMA systems, this study adopts a particle filter to estimate the fractional part of the PDISBs (F-PDISBs) between the systems and proposes a particle filter-based inter-system positioning model. Results show that the F-PDISBs and code DISBs for the baselines with the same receiver types and some with different receiver types are rather stable over time and for these baselines it is feasible to use a particle filter to estimate the F-PDISB parameters in the initial stage. Having attained the F-PDISBs, the inter-system model can be constructed to improve positioning accuracy in complex operational environments.

Dynamic surface control for nonlinear fractional-order chaotic systems with time delays

Due to the uncertainty and randomness, the chaotic behavior of nonlinear system has become an important factor affecting the stability of the system. Therefore, it is very necessary to ensure that the nonlinear system has strong robustness against the external stochastic interference. This paper presents a new dynamic surface controller for nonlinear systems with time delays. In order to be closer to the actual engineering complex operation environment, the fractional calculus operator is added in the system. The dynamic surface control and time-delay effect is first applied to the second-order nonlinear fractional-order system. To deal with the problem of number explosion and time-delay in traditional inversion methods, a fractional-order filter and an alpha-order Pade approximation method are designed. After fully considering the tracking error, virtual control error and filtering error, the dynamic surface controller is established and its stability is verified. It can be seen from the simulation results that the system tracks the set function trajectory in finite time, and the controller has a good effect.

International Conference on Road Tunnel Safety and Risk Prevention and Control (ICRTSR 2019)

Preface The tunnel is an important transportation infrastructure. By the year of 2018, there were 17738 highway tunnels in China, 17236.1 kilometers long, thousands of urban tunnels and tens of thousands of railway tunnels. The tunnel is affected by the surrounding buildings, the crowded vehicles, the complex operation environment, the increase of bad disaster and the insufficient management level, which make the safety of operation and maintenance of the tunnel prominent and sensitive to the social impact. Therefore, it is particularly urgent and important to improve its operation and maintenance safety level. In the past few years, China, like other countries in the world, has seen a lot of accidents in tunnel operation and maintenance. At the same time, a lot of new technology for tunnel operation and maintenance has been put forward. This conference will provide a platform for scholars and tunnel managers all over the world to share the latest research results and management methods, and work together to improve the safety level of tunnel operation and maintenance.The Organizing Committee of ICRTSR 2019 is pleased to announce that the International Conference on Road Tunnel Safety and Risk Prevention and Control (ICRTSR 2019) has been held in Chongqing, China during December 05-07, 2019.All papers submitted at the conference have been through rigorous review and process by the conference committee, and a volume of high-quality papers were selected for oral presentation and publication at the conference. We hope these books will serve as a reference for readers.We would like to thank the conference committee for reviewing the papers impartially and responsibly. We also want to thank everyone and related institutions for their support and help. We sincerely hope the 2019 International Conference on Road Tunnel Safety and Risk Prevention and Control (ICRTSR 2019) will become a top conference, and we are convinced that the proceedings of this conference will be an important study reference and knowledge source, which can not only promote the progress of science and engineering, but also boost the development of other new products and processes.The Organizing Committee of ICRTSR 2019List of Committee member, Scientific Committee, Chairman, Vice chairman, Committeeman, Organizing Committee, Chairman, Vice chairman, Member are available in this pdf.

An Integrated MCDM Approach to Train Derailment Risk Response Strategy Selection

Train derailment can mainly cause not only economic losses in the shape of mangled rolling stock or infrastructure, but also more severely in causalities and disruptions of operations, yielding great impact on the sustainable development of railway industry. Considering various complex and symmetrical operational environments, as well as the characteristics of low frequency and high consequences of derailment accidents, risk response is undoubtedly underlined as one of the most critical components of risk management process. However, in practice, risk response does not receive enough attention in comparison with risk assessment that it lacks mature models and tools for selecting optimal strategy. This study constructs an integrated Multi Criteria Decision-Making (MCDM) model for the selection of optimum train derailment risk response strategy for the first time. In the model, decision making trial and evaluation laboratory (DEMATEL) technology is connected with analytical network process (ANP) to obtain evaluation criteria and their relative weights, and both of the two methods can deal with the complex coupling relationship between the indicators in the system. Then, technique for order performance by similarity to ideal solution (TOPSIS) is proposed to screen the optimum plan in the proposed model. Further, the Delphi method is used through the whole process to acquire expert advice. In the end, this model is used to select shunting derailment risk response strategies in Huangyangcheng station, and the final results demonstrate that this technology is simple and practical, and can provide a credible and practical tool for railway safety managers and engineers to choose the best risk response strategy.

Structure and an unstable business operating environment: Revisiting Burns and Stalker’s organisation-environment theory in Zimbabwe’s manufacturing sector

Background: Turbulent socioeconomic contexts coupled with volatile political environments pose a serious survival threat to business organisations. Complex operational environment of this dimension most often resists application of conventional management theories and practices. Organisational managers are therefore constantly challenged to adopt contingency strategies that will not only keep their organisations afloat, but also entrench competitive advantage that could effectively sustain operations. Aim: To update Burns and Stalker’s theory on structure and business environments. Setting: The dynamics of the Zimbabwe’s economy has assumed an extraordinary proportion of complexity due to intractable political instability and hostile economic environment. Methods: Using a survey research design and employing quantitative research strategy, this article examines the underlining propositions that defined the seminal work of Burns and Stalker regarding strategy adoption by organisations in a dynamic operating environment. Primary data was collected from 189 randomly selected managers in 350 manufacturing firms operating in Zimbabwe using a structured questionnaire. Data were analysed using structural equation modelling. Results: The major finding of the present study suggests that firms adopt a hybrid structure when confronted with an unstable operating environment. Conclusion: The finding is inconsistent with that of Burns and Stalker, who concluded that firms adopt organic structure in an unstable operating environment. While Burns and Stalker’s study was conducted in a relatively stable socioeconomic context, the present study was conducted in an operating environment that is characterised by turbulent socioeconomic and political instability. These environmental divergences could have influenced the outcome of both studies.

Dynamic task assignment method based on NSGA-II algorithm in command and control field

In order to improve the adaptability of command and control organizations to complex operational environments and effectively solve the unexpected events that may be encountered in the operation of C2 organizations, this paper studies the dynamic task assignment of C2 organizations and proposes multi-objective tasks assignment method based on NSGA-II algorithm. Firstly, two kinds of emergencies such as platform damage and task increase in the course of combat are analyzed, and the mathematical expression method is given. Secondly, a multi-objective optimization model aiming at maximizing task completion quality and minimizing plan adjustment cost is constructed. And the adaptive NSGA-II algorithm is designed to solve the model. Finally, the simulation experiment shows that the constructed model can effectively deal with the above-mentioned emergencies, and the designed algorithm can obtain the better Pareto frontier and realize the dynamic task assignment.

Teleoperation for space manipulator based on complex virtual fixtures

This paper presents a kind of complex virtual fixture (VF) to help space robots perform on-orbit operations in complex environments while ensuring operations safety. The main purpose of the VF is to provide virtual force feedback to adjust the manners of the operator throughout the remote operation process. The complex VF is comprised of a tube-type VF and a velocity-based VF. The tube-type VF ensures that the end effector approaches the target safely and at high speeds over long distances, and the velocity-based VF enables the end of the robot to observe the target within a safe and short distance near the target. Combined with dynamic prediction and path planning, the complex VF can improve the flexibility and efficiency of the operation, and avoid collisions in dynamic environments. The proposed methods are verified by several typical space manipulation tasks, the virtual experiment environment of which is built in CHAI3D. The comparative results indicate that the complex VF can reduce operation time and improve efficiency and accuracy.

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments.

Sleep loss and circadian misalignment contribute to a meaningful proportion of operational accidents and incidents. Countermeasures and work scheduling designs aimed at mitigating fatigue are typically evaluated in controlled laboratory environments, but the effectiveness of translating such strategies to operational environments can be challenging to assess. This manuscript summarizes an approach for collecting sleep, circadian, fatigue, and performance data in a complex operational environment. We studied 44 airline pilots over 34 days while they flew a fixed schedule, which included a baseline data collection with 5 days of mid-morning flights, four early flights, four high-workload mid-day flights, and four late flights that landed after midnight. Each work block was separated by 3-4 days of rest. To assess sleep, participants wore a wrist-worn research-validated activity monitor continuously and completed daily sleep diaries. To assess the circadian phase, pilots were asked to collect all urine produced in four or eight hourly bins during the 24 h after each duty block for the assessment of 6-sulfatoxymelatonin (aMT6s), which is a biomarker of the circadian rhythm. To assess subjective fatigue and objective performance, participants were provided with a touch-screen device used to complete the Samn-Perelli Fatigue Scale and Psychomotor Vigilance Task (PVT) during and after each flight, and at waketime, mid-day, and bedtime. Using these methods, it was found that sleep duration was reduced during early starts and late finishes relative to baseline. Circadian phase shifted according to duty schedule, but there was a wide range in the aMT6s peak between individuals on each schedule. PVT performance was worse on the early, high-workload, and late schedules relative to baseline. Overall, the combination of these methods was practical and effective for assessing the influence of sleep loss and circadian phase on fatigue and performance in a complex operational environment.

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

Cooperative Prescribed Performance Tracking Control for Multiple High-Speed Trains in Moving Block Signaling System

If high-speed trains move under a moving block signaling (MBS) system, it is a quite challengeable and significant issue to cooperate the multiple high-speed trains, such that they are separated safely and governed steadily. As an intelligent, comprehensive, and advanced modern train operation control system, the MBS system will definitely, in the near future, replace the widely used fixed block signaling system due to its modern communication, multi-source positioning, and advanced control method. Due to the involvement of several subsystems and lots of track-side equipment, such as radio block center (RBC), global system for mobile communications-railway (GSM-R), and so on, it is a nontrivial task to develop a reliable MBS system under complex operational environments, particularly without a pivotally cooperative control method for the movement of multiple high-speed trains. This paper addresses the cooperative control for multiple high-speed trains to achieve prescribed performance tracking, i.e., the speed and the position of high-speed trains are guaranteed to be confined to specific speed limitations and allowed distances ratified by automatic train protection and moving authority, respectively. The proposed control requires no prior information of the empirical parameters of the operational resistances and online adjusts by proper adaptation laws. Theoretical analysis and simulation results are given to demonstrate the effectiveness of the proposed control methods.

Fostering entrepreneurial innovation ecosystems: lessons learned from the European Institute of Innovation and Technology

While business practices and management research has evolved towards more systemic approaches building on the notion of ecosystem to relate organisations with their complex operational environment, the linear model of innovation is still highly pervasive in governmental innovation policies. In this paper we contribute to the narrowing the research gap in the field on ecosystems by combining two often separate streams of literature and subsequently coining the term ‘entrepreneurial innovation ecosystems’. We then examine the European Institute of Innovation and Technology (EIT), a recent policy experiment aimed at developing Pan-European ecosystems to stimulate high-impact innovative entrepreneurship, which is done by integrating higher education, research and business activities (i.e. via knowledge triangle integration). While the high political profile of the EIT has constrained partly its freedom to experiment, a European-wide networked excellence and business logic in managing KICs has created new insights on experimental governance models that merit to be explored further. Building on an action research case study, the paper documents these developments and opens up an avenue for further work regarding experimental governance of Pan-European entrepreneurial innovation ecosystems.

Maize seedling detection under different growth stages and complex field environments based on an improved Faster R–CNN

This paper presents an improved Faster R–CNN model for a field robot platform (FRP) aimed at automatically extracting image features and quickly and accurately detecting maize seedlings during different growth stages under complex field operation environments, with the goal of preparing for intelligent inter-tillage in maize fields. A FRP with five industrial USB cameras for data collection was used to capture a large number of sample images. The shooting angle range of the industrial USB cameras is 0–90°. The photographs were used to create an image database containing twenty thousand images of soil, maize and weeds. Ten selected pretrained networks were used to replace the network of the CNN feature computing component of the classic Faster R–CNN. A Faster R–CNN with VGG19 processed by the pretrained networks method is proposed. The Faster R–CNN algorithm used in this work represents a deep learning architecture that distinguishes maize seedlings and weeds under three field conditions: Full-cycle, Multi-weather and Multi-angle. This work achieved greater than 97.71% precision in the detection of maize seedlings with respect to soil and weeds. The precision rate of six-leaf to seven-leaf maize seedlings was 2.74% lower than that of the total test set. The precision rate under sunny conditions was 1.97% lower than that of the total test set. The precision rate of an angle shot of 0° was 0.95% lower than that of the total test set. The proposed model has significant potential for autonomous weed and maize classification under actual operating conditions.

A new integrated collision risk assessment methodology for autonomous vehicles

Real-time risk assessment of autonomous driving at tactical and operational levels is extremely challenging since both contextual and circumferential factors should concurrently be considered. Recent methods have started to simultaneously treat the context of the traffic environment along with vehicle dynamics. In particular, interaction-aware motion models that take inter-vehicle dependencies into account by utilizing the Bayesian interference are employed to mutually control multiple factors. However, communications between vehicles are often assumed and the developed models are required many parameters to be tuned. Consequently, they are computationally very demanding. Even in the cases where these desiderata are fulfilled, current approaches cannot cope with a large volume of sequential data from organically changing traffic scenarios, especially in highly complex operational environments such as dense urban areas with heterogeneous road users. To overcome these limitations, this paper develops a new risk assessment methodology that integrates a network-level collision estimate with a vehicle-based risk estimate in real-time under the joint framework of interaction-aware motion models and Dynamic Bayesian Networks (DBN). Following the formulation and explanation of the required functions, machine learning classifiers were utilized for the real-time network-level collision prediction and the results were then incorporated into the integrated DBN model for predicting collision probabilities in real-time. Results indicated an enhancement of the interaction-aware model by up to 10%, when traffic conditions are deemed as collision-prone. Hence, it was concluded that a well-calibrated collision prediction classifier provides a crucial hint for better risk perception by autonomous vehicles.

Risk-informed decision-making in asset management as a complex adaptive system of systems

Decision-making is an essential activity in asset management (AM). It is influenced by various factors (strategic, technical/technological, economic, organisational, regulatory, safety, markets, etc.). Sound decision-making in AM ought to take into account relevant factors in order to balance risks, opportunities, performance, costs and benefits. Additionally, modern organisations evolve in complex operational and business environments and are exposed to significant uncertainties. In such a context, decision-making in AM becomes more challenging. This study proposes a holistic three-step risk-informed decision-making (RIDM) methodology developed for AM, where RIDM is considered a complex adaptive system of systems. The methodology is applied in a case study to analyse possible modification strategies for a nuclear power plant's emergency core cooling system. Through the RIDM process, quantitative models and other factors have been taken into account in order to obtain the necessary comprehensive insights regarding the decision to be made.

Broad Utility: Architecting Flexible and Robust Systems for a Complex Operational Environment

The current and future Operational Environment (OE) for the United States (U.S.) military is becoming increasingly complex. This complexity requires Systems Engineers and Architects to develop new approaches for evaluating the variability inherent in the OEs of today and tomorrow. In response to this growing capability gap, the U.S. Department of Defense (DoD) established the Engineered Resilient Systems (ERS) program. A core property of ERS is Broad Utility—the ability of a system to, “perform effectively in a wide range of operations across multiple futures despite experiencing disruptions.” [1] This paper discusses on-going research intended to provide system designers with an approach to architecting systems for Broad Utility. Specifically, this research seeks to accomplish three objectives: (1) Identify gaps in current U.S. DoD doctrine impacting the ability to architect for Broad Utility; (2) Develop an integrated network model of the Operational Environment that highlights how OE variables can impact system effectiveness; and (3) Propose an approach for ensuring that system architectures exhibit Broad Utility, through a process of mapping system Flexibility and Robustness to the variables of the OE. By employing this approach early in the Systems Engineering process, system designers can increase the likelihood that the resulting system responds appropriately to a changing environment.

Design and Implementation of Plastic Deformation Behavior by Cartesian Impedance Control Based on Maxwell Model

Compliance has become one prerequisite of robots designed to work in complex operation environment where dynamic and uncertain physical contact or impact takes place frequently and even intentionally. Impedance control is a typical complaint control methodology. Standard impedance control is based on dynamics described by a spring and damper model connected in parallel way, which endues the robot an elastic behavior. In contrast, plastic deformation can be realized by Maxwell model in which spring and damper connect in series. In this study, a novel Cartesian impedance controller is constructed based on the Maxwell model. Implementation in a robot manipulator is executed to validate and analyze the proposed control law. A plastic deformation behavior of the robot manipulator is produced and certain extent compliance is achieved under the unpredictable impact or contact force exerted by human or other environment objects.

Online health assessment of wind turbine based on operational condition recognition

To reduce the operation and maintenance (O&M) costs, the health assessment of wind turbine has received more and more attention in recent years. However, it is difficult to evaluate the health condition of wind turbine due to the complex and non-stationary operation environment. This paper proposes a data-driven approach for online health assessment of wind turbine based on operational condition recognition. First, the operational condition parameters are selected by analyzing the monitoring data of wind turbine. Considering the time-varying of operation environment, the operational conditions are divided into four subspaces utilizing the K-means clustering algorithm. Then, using the historical state parameters data under normal operation, a health benchmark model is constructed in each operational condition space based on Gaussian Mixture Model (GMM). Further, a Softmax model is trained according to the results of operational condition classification, which is used to identify the online operational condition of wind turbine. Moreover, an overall health index (HI) based on Mahalanobis distance is developed to assess the health condition of wind turbine. Finally, the method is verified by the actual supervisory control and data acquisition (SCADA) data of a wind field in northwestern China. The test results show that the proposed approach can track the running state of the wind turbine accurately and play a good role in early fault warning.

The Research on Comprehensive Performance Evaluation of Axial Piston Pump Based on AHP

The axial piston pump is an important industrial power element, and its performance directly affects the operation of the system. However, owing to its complex structure and harsh operational environment, the actual operational performance of the axial piston pump is difficult to be assessed accurately, which makes ensuring the normal operation of the hydraulic system difficult. To improve the evaluation method for an axial piston pump, a comprehensive performance evaluation system was proposed based on analytic hierarchy process (AHP), which could assess the performance of an axial piston pump on the basis of theoretical analysis, test, operator interaction, and application. Considering a model of load-sensitive axial piston pump with good operability as an example, the model development and simulation of the pump were carried out based on AMESim and the pump was tested using a developed performance test bed for axial piston pump. The weights of factors in the evaluation system were determined on the basis of the simulation results, analyses, and calculation. The above results were used to comprehensively analyze the tested pump and design a set of comprehensive performance evaluation software. The evaluation result was nearly identical to the actual usage, which verified the feasibility of the designed evaluation system.

Multi-Sensor Based Online Attitude Estimation and Stability Measurement of Articulated Heavy Vehicles.

Articulated wheel loaders used in the construction industry are heavy vehicles and have poor stability and a high rate of accidents because of the unpredictable changes of their body posture, mass and centroid position in complex operation environments. This paper presents a novel distributed multi-sensor system for real-time attitude estimation and stability measurement of articulated wheel loaders to improve their safety and stability. Four attitude and heading reference systems (AHRS) are constructed using micro-electro-mechanical system (MEMS) sensors, and installed on the front body, rear body, rear axis and boom of an articulated wheel loader to detect its attitude. A complementary filtering algorithm is deployed for sensor data fusion in the system so that steady state margin angle (SSMA) can be measured in real time and used as the judge index of rollover stability. Experiments are conducted on a prototype wheel loader, and results show that the proposed multi-sensor system is able to detect potential unstable states of an articulated wheel loader in real-time and with high accuracy.