• Sound Level Monitoring at Live Events, Part 3 - Improved Tools and Procedures

      Hill, Adam J.; Mulder, Johannes; Burton, Jon; Kok, Marcel; Lawrence, Michael; University of Derby; The Australian National University; Rational Acoustics; dBcontrol (Audio Engineering Society, 2022-01-23)
      This is the final installment in a series of three papers looking into the subject of sound level monitoring at live events. The first two papers revealed how practical shortcomings and audience and neighbor considerations (in the form of sound level limits) can impact the overall live experience. This paper focuses on an improved set of tools for sound engineers to ensure a high-quality and safe live event experience while maintaining compliance with local sound level limits. This includes data processing tools to predict future limit violations and guidelines for improved user interface design. Practical procedures, including effective sound level monitoring practice, alongside resourceful mixing techniques are presented to provide a robust toolset that can allow sound engineers to perform their best without compromising the listening experience in response to local sound level limits.
    • Sound Level Monitoring at Live Events, Part 2 - Regulations, Practices, and Preferences

      Hill, Adam J.; Mulder, Johannes; Burton, Jon; Kok, Marcel; Lawrence, Michael; University of Derby; The Australian National University; Rational Acoustics; dBcontrol (Audio Engineering Society, 2022-01-23)
      This paper considers existing regulations, practices, and preferences regarding the measurement, monitoring, and management of sound levels at live music events. It brings together a brief overview of current regulations with the outcomes of a recent international survey of live sound engineers and evaluation of three datasets of sound measurement at live music events. The paper reveals the benefit of a 15-min time frame for the definition of equivalent continuous sound level limits in comparison to longer or shorter time frames. The paper also reveals support from the live sound engineering community for the application of sound level limits and development of a global certification system for live sound engineers.
    • Relations Between Entropy and Accuracy Trends in Complex Artificial Neural Networks

      Cavallaro, Lucia; Grassia, Marco; Fiumara, Giacomo; Mangioni, Giuseppe; De Meo, Pasquale; Carchiolo, Vincenza; Bagdasar, Ovidiu; Liotta, Antonio; University of Derby; Università degli Studi di Catania, Italy; et al. (Springer, 2022-01-01)
      Training Artificial Neural Networks (ANNs) is a non-trivial task. In the last years, there has been a growing interest in the academic community in understanding how those structures work and what strategies can be adopted to improve the efficiency of the trained models. Thus, the novel approach proposed in this paper is the inclusion of the entropy metric to analyse the training process. Herein, indeed, an investigation on the accuracy computation process in relation to the entropy of the intra-layers’ weights of multilayer perceptron (MLP) networks is proposed. From the analysis conducted on two well-known datasets with several configurations of the ANNs, we discovered that there is a connection between those two metrics (i.e., accuracy and entropy). These promising results can be helpful in defining, in the future, new criteria to evaluate the training process goodness in real-time by optimising it and allow faster detection of its trend.
    • Multilevel Inverter for Hybrid Fuel Cell/PV Energy Conversion System

      Fekik, Arezki; Hamida, Mohamed Lamine; Denoun, Hakim; Azar, Ahmad Taher; Kamal, Nashwa Ahmad; Amar, Bousbaine; Benamrouche, Nacereddine; University, Bouira, Algeria; Mouloud Mammeri University, Algeria; Prince Sultan University, Riyadh, Saudi Arabia; et al. (IGI Global, 2022)
      Power converters assume a significant part in fuel cell power generation systems and solar power conversion systems which are an alternative to fossil fuel production systems. There is therefore a demand for high quality power conditioning used in PEMFC systems and photovoltaic panels. This chapter proposes a hybrid electric power (FC/PV) production strategy with the use of converter topology as the power interface and also introduces a three-level inverter topology for different operating levels. The converter increases the input voltage to the rated voltage and turns into a DC bus; the multi-level inverter converts the voltage to AC and supplies AC loads. This chapter develops a hybrid electric power generation strategy, which can produce output with positive and zero sequences. Integrating the three-stage inverter into the hybrid renewable energy (FC/PV) production system allows for near sinusoidal current with low THD. The topology of hybrid energy production using the multi-level converter is tested on Matlab.
    • Embedded Data Imputation for Environmental Intelligent Sensing: A Case Study

      Erhan, Laura; Di Mauro, Mario; Anjum, Ashiq; Bagdasar, Ovidiu; Song, Wei; Liotta, Antonio; University of Derby; University of Salerno, 84084 Fisciano, Italy; University of Leicester; University of Alba Iulia, 510009 Alba Iulia, Romania; et al. (MDPI AG, 2021-11-23)
      Recent developments in cloud computing and the Internet of Things have enabled smart environments, in terms of both monitoring and actuation. Unfortunately, this often results in unsustainable cloud-based solutions, whereby, in the interest of simplicity, a wealth of raw (unprocessed) data are pushed from sensor nodes to the cloud. Herein, we advocate the use of machine learning at sensor nodes to perform essential data-cleaning operations, to avoid the transmission of corrupted (often unusable) data to the cloud. Starting from a public pollution dataset, we investigate how two machine learning techniques (kNN and missForest) may be embedded on Raspberry Pi to perform data imputation, without impacting the data collection process. Our experimental results demonstrate the accuracy and computational efficiency of edge-learning methods for filling in missing data values in corrupted data series. We find that kNN and missForest correctly impute up to 40% of randomly distributed missing values, with a density distribution of values that is indistinguishable from the benchmark. We also show a trade-off analysis for the case of bursty missing values, with recoverable blocks of up to 100 samples. Computation times are shorter than sampling periods, allowing for data imputation at the edge in a timely manner.
    • WHAM: To Asymmetry and Beyond!

      Dring, Mark; Wiggins, Bruce; University of Derby (Institute of Acoustics, 2021-11-18)
      Auralisation of acoustic spaces is a tool used in many industries. To provide a truly representative result, the systems used must capture and deliver critical, dynamic, psychoacoustic cues that react to the listeners head position. The WHAM (Webcam Head-tracked Ambisonics) website (www.brucewiggins.co.uk/WHAM) utilises webcams to provide auralisation that reacts to head rotation via the browser using standard HRTF data; visitors to the site can experience very high order horizontal only Ambisonic to binaural presentation of room responses. In its initial inception, orders were limited to 7th order asymmetry for the final binaural presentation, which previous research has shown to fall below a transparent perceptual threshold compared to orders up to 31st. This paper documents the developments to deliver beyond 7th order and improvements in functionality made to the WHAM website and open-source JS Ambisonics software library, that continue to make it a useful remote resource for acoustic auralisation purposes.
    • Sound Level Monitoring at Live Events, Part 1–Live Dynamic Range

      Hill, Adam J.; Mulder, Johannes; Burton, Jon; Kok, Marcel; Lawrence, Michael; University of Derby; The National University of Australia; dBcontrol; Rational Acoustics (Audio Engineering Society, 2021-11-08)
      Musical dynamics are often central within pieces of music and are therefore likely to be fundamental to the live event listening experience. While metrics exist in broadcasting and recording to quantify dynamics, such measures work on high-resolution data. Live event sound level monitoring data is typically low-resolution (logged at one second intervals or less), which necessitates bespoke musical dynamics quantification. Live dynamic range (LDR) is presented and validated here to serve this purpose, where measurement data is conditioned to remove song breaks and sound level regulation-imposed adjustments to extract the true musical dynamics from a live performance. Results show consistent objective performance of the algorithm, as tested on synthetic data as well as datasets from previous performances.
    • Closed-Loop Nash Equilibrium in the Class of Piecewise Constant Strategies in a Linear State Feedback Form for Stochastic LQ Games

      Drăgan, Vasile; Ivanov, Ivan Ganchev; Popa, Ioan-Lucian; Bagdasar, Ovidiu; Romanian Academy, Bucharest, Romania; The Academy of the Romanian Scientists, Bucharest, Romania; Sofia University St. Kliment Ohridski, Bulgaria; University of Alba Iulia, Romania; University of Derby (MDPI AG, 2021-10-26)
      In this paper, we examine a sampled-data Nash equilibrium strategy for a stochastic linear quadratic (LQ) differential game, in which admissible strategies are assumed to be constant on the interval between consecutive measurements. Our solution first involves transforming the problem into a linear stochastic system with finite jumps. This allows us to obtain necessary and sufficient conditions assuring the existence of a sampled-data Nash equilibrium strategy, extending earlier results to a general context with more than two players. Furthermore, we provide a numerical algorithm for calculating the feedback matrices of the Nash equilibrium strategies. Finally, we illustrate the effectiveness of the proposed algorithm by two numerical examples. As both situations highlight a stabilization effect, this confirms the efficiency of our approach.
    • A mixed-integer linear programming formulation for the modular layout of three-dimensional connected systems

      O’Neill, Sam; Wrigley, Paul; Bagdasar, Ovidiu; University of Derby (Elsevier BV, 2021-10-02)
      Given the considerable complexity of process plants, there has been a great deal of research focused on aiding the design of plant layout through mathematical optimisation, i.e. optimising the positioning of the equipment in the plant for space and cost efficiency. Recently, the use of modular approaches within the construction industry, whereby work is performed off-site before being assembled on-site, has become a popular and powerful way of reducing build schedules and costs. Modular approaches have many other real applications where items must be packed to minimise the connections between them (e.g. piping, wiring, modular office and factory layouts) and consider the modular layout of the system. In this paper, we provide a formulation of the problem that, in addition to the standard layout problem, considers a modular block layout to allow modular construction and transportation of the plant. The problem is represented as a directed network, with the aim to pack the items into predefined containers and minimise the rectilinear distance between the connected items. We propose mixed-integer linear programming (MILP) models for the 2-dimensional and 3-dimensional problems and solve them using the state-of-the-art mathematical programming solver, Gurobi. Because of the combinatorial nature of the problem, solutions involving a large number of items may not converge and a suboptimal solution must be considered. However, our results suggest that even in the case of optimising a large number of items, the suboptimal solutions found after a reasonable number of iterations where deemed, by a domain expert, to be a good enough starting point to continue the design process, especially in the early concept phase.
    • Social network analysis: the use of graph distances to compare artificial and criminal networks

      Ficara, Annamaria; Curreri, Francesco; Cavallaro, Lucia; De Meo, Pasquale; Fiumara, Giacomo; Bagdasar, Ovidiu; Liotta, Antonio; University of Palermo, Palermo, Italy.; University of Messina, Messina, Italy.; University of Derby; et al. (OAE Publishing Inc., 2021-09-28)
      Aim: Italian criminal groups become more and more dangerous spreading their activities into new sectors. A criminal group is made up of networks of hundreds of family gangs which extended their influence across the world, raking in billions from drug trafficking, extortion and money laundering. We focus in particular on the analysis of the social structure of two Sicilian crime families and we used a Social Network Analysis approach to study the social phenomena. Starting from a real criminal network extracted from meetings emerging from the police physical surveillance during 2000s, we here aim to create artificial models that present similar properties. Methods: We use specific tools of social network analysis and graph theory such as network models (i.e., random, small-world and scale-free) and graph distances to quantify the similarity between an artificial network and a real one. To the best of our knowledge, spectral graph distances and the DeltaCon similarity have never been applied to criminal networks. Results: Our experiments identify the Barabási-Albert model as the one which better represents a criminal network. For this reason, we could expect that new members of a criminal organization will be more likely to establish connections with high degree nodes rather than low degree nodes. Conclusion: Artificial but realistic models can represent a useful tool for Law Enforcement Agencies to simulate and study the structure, evolution and faults of criminal networks.
    • Mobility Analysis during the 2020 Pandemic in a Touristic city: the Case of Cagliari

      Ferrara, Enrico; Uras, Marco; Atzori, Luigi; Bagdasar, Ovidiu; Liotta, Antonio; University of Derby; University of Cagliari; Free University of Bozen-Bolzano, Italy (IEEE, 2021-09-20)
      The impact of the 2020 COVID-19 pandemic has been significant on every aspect of life and has drastically changed our habits. Here we analyze an extensive set of traffic traces in Cagliari, one of the most touristic cities in the Mediterranean, to quantify how the different phases of the pandemic have affected not only traffic volumes but also their patterns. We put traffic in relation to different restriction levels, finding a non-linear relation. Following a 76% traffic reduction on the first lockdown, subsequent restrictions have lead to less sudden changes. We then use the official tourist-presence figures to pinpoint the traffic stations that are influenced by tourists’ mobility the most. All in all, our analysis shows that although the absolute traffic volumes roughly followed the pandemic evolution, the weekly traffic patterns changed drastically over the time, whereas the daily ones maintained more consistency.
    • A Novel Security Methodology for Smart Grids: A Case Study of Microcomputer-Based Encryption for PMU Devices

      Varan, Metin; Akgul, Akif; Kurugollu, Fatih; Sansli, Ahmet; Smith, Kim; University of Applied Sciences, Serdivan 54050, Sakarya, Turkey; Hitit University, Corum 19030, Turkey; University of Derby (Hindawi Limited, 2021-09-18)
      Coordination of a power system with the phasor measurement devices (PMUs) in real time on the load and generation sides is carried out within the context of smart grid studies. Power systems equipped with information systems in a smart grid pace with external security threats. Developing a smart grid which can resist against cyber threats is considered indispensable for the uninterrupted operation. In this study, a two-way secure communication methodology underpinned by a chaos-based encryption algorithm for PMU devices is proposed. (e proposed system uses the IEEE-14 busbar system on which the optimum PMU placement has been installed. (e proposed hyperchaotic system-based encryption method is applied as a new security methodology among PMU devices. (e success of results is evaluated by the completeness of data exchange, durations, the complexity of encryption-decryption processes, and strength of cryptography using a microcomputer-based implementation. (e results show that the proposed microcomputer-based encryption algorithms can be directly embedded as encryption hardware units into PMU and PDC devices which have very fast signal processing capabilities taking into considerations the acceptable delay time for power system protection and measuring applications and quality metering applications which is 2 ms and 10 ms, respectively. While proposed algorithms can be used in TCP or UDP over IP-based IEEE C37.118, IEC 61850, and IEC 61850-90-5 communication frameworks, they can also be embedded into electronic cards, smartcards, or smart tokens which are utilized for authentication among smart grid components.
    • Mixed Time-Delayed Nonlinear Multi-agent Dynamic Systems for Asymptotic Stability and Non-fragile Synchronization Criteria

      Arockia, Stephen; Raja, R; Alzabut, J; Zhu, Quanxin; Niezabitowski, M; Bagdasar, Ovidiu; Alagappa University, Karaikudi, India; Prince Sultan University, Riyadh, 12435, Saudi Arabia; OSTİM Technical University, Ankara, 06374, Turkey; Silesian University of Technology, Akademicka 16, Gliwice, 44-100, Poland; et al. (Springer, 2021-09-13)
      In this manuscript, we are concerned with mixed (discrete and distributed) time-delayed both stability and non-fragile synchronization of nonlinear multi-agent systems (MASs). We shall find stability criteria for the unknown parameter value of nonlinear multi-agent systems using the Lyapunov–Krasovskii functions, Lemma, the analytical techniques, the Kronecker product, and the general specifications for asymptotic stability of selected MASs are obtained. Moreover, criteria for the synchronization of leader–follower unknown parameter value of nonlinear MASs with non-fragile controllers are discussed. At last, we provide two numerical calculations along with the computational simulations to check the validity of the theoretical findings reported in this manuscript.
    • Advances in Manufacturing Technology XXXIV

      Shafik, Mahmoud; Case, Keith; University of Derby (IOS Press, 2021-09-07)
      The development of technologies and management of operations is key to sustaining the success of manufacturing businesses, and since the late 1970s, the International Conference on Manufacturing Research (ICMR) has been a major annual event for academics and industrialists engaged in manufacturing research. The conference is renowned as a friendly and inclusive platform that brings together a broad community of researchers who share a common goal. This book presents the proceedings of ICMR2021, the 18th International Conference on Manufacturing Research, incorporating the 35th National Conference on Manufacturing Research, and held in Derby, UK, from 7 to 10 September 2021. The theme of the ICMR2021 conference is digital manufacturing. Within the context of Industrial 4.0, ICMR2021 provided a platform for researchers, academics and industrialists to share their vision, knowledge and experience, and to discuss emerging trends and new challenges in the field. The 60 papers included in the book are divided into 10 parts, each covering a different area of manufacturing research. These are: digital manufacturing, smart manufacturing; additive manufacturing; robotics and industrial automation; composite manufacturing; machining processes; product design and development; information and knowledge management; lean and quality management; and decision support and production optimization. The book will be of interest to all those involved in developing and managing new techniques in manufacturing industry.
    • A systematic literature review of machine learning applications for community-acquired pneumonia

      Lozano-Rojas, Daniel; Free, Robert C.; McEwan, Alistair A.; Woltmann, Gerrit; University of Leicester; University of Derby; University Hospitals of Leicester NHS Trust, Leicester (Springer, 2021-08-15)
      Community acquired pneumonia (CAP) is an acute respiratory disease with a high mortality rate. CAP management follows clinical and radiological diagnosis, severity evaluation and standardised treatment protocols. Although established in practice, protocols are labour intensive, time-critical and can be error prone, as their effectiveness depends on clinical expertise. Thus, an approach for capturing clinical expertise in a more analytical way is desirable both in terms of cost, expediency, and patient outcome. This paper presents a systematic literature review of Machine Learning (ML) applied to CAP. A search of three scholarly international databases revealed 23 relevant peer reviewed studies, that were categorised and evaluated relative to clinical output. Results show interest in the application of ML to CAP, particularly in image processing for diagnosis, and an opportunity for further investigation in the application of ML; both for patient outcome prediction and treatment allocation. We conclude our review by identifying potential areas for future research in applying ML to improve CAP management. This research was co-funded by the NIHR Leicester Biomedical Research Centre and the University of Leicester.
    • Criminal networks analysis in missing data scenarios through graph distances

      Ficara, Annamaria; Cavallaro, Lucia; Curreri, Francesco; Fiumara, Giacomo; De Meo, Pasquale; Bagdasar, Ovidiu; Song, Wei; Liotta, Antonio; University of Palermo, Palermo, Italy; University of Messina, Messina, Italy (Public Library of Science (PLoS), 2021-08-11)
      Data collected in criminal investigations may suffer from issues like: (i) incompleteness, due to the covert nature of criminal organizations; (ii) incorrectness, caused by either unintentional data collection errors or intentional deception by criminals; (iii) inconsistency, when the same information is collected into law enforcement databases multiple times, or in different formats. In this paper we analyze nine real criminal networks of different nature (i.e., Mafia networks, criminal street gangs and terrorist organizations) in order to quantify the impact of incomplete data, and to determine which network type is most affected by it. The networks are firstly pruned using two specific methods: (i) random edge removal, simulating the scenario in which the Law Enforcement Agencies fail to intercept some calls, or to spot sporadic meetings among suspects; (ii) node removal, modeling the situation in which some suspects cannot be intercepted or investigated. Finally we compute spectral distances (i.e., Adjacency, Laplacian and normalized Laplacian Spectral Distances) and matrix distances (i.e., Root Euclidean Distance) between the complete and pruned networks, which we compare using statistical analysis. Our investigation identifies two main features: first, the overall understanding of the criminal networks remains high even with incomplete data on criminal interactions (i.e., when 10% of edges are removed); second, removing even a small fraction of suspects not investigated (i.e., 2% of nodes are removed) may lead to significant misinterpretation of the overall network.
    • Education and Certification in Sound Pressure Level Measurement, Monitoring and Management at Entertainment Events

      Mulder, Johannes; Hill, Adam; Burton, Jon; Kok, Marcel; Lawrence, Michael; Shabalina, Elena; University of Derby; National University of Australia; dBcontrol; Rational Acoustics; et al. (Audio Engineering Society, 2021-07-22)
      A recent AES Technical Document on sound exposure and noise pollution due to outdoor music events proposes the creation of a live event sound level management initiative. In parallel, the World Health Organization, by way of the Make Listening Safe initiative, is preparing a regulatory framework for control of recreational sound exposure in entertainment venues. This paper considers how these developments could inform a certification scheme for live sound engineers and other key stakeholders. Such a scheme would detail current best practice and would allow venues, events, manufacturers and performers to voluntarily gain certification. This would help to boost public visibility of what an event or venue has done to promote the health and wellbeing of all key stakeholders.
    • A collaborative approach for national cybersecurity incident management

      Oriola, Oluwafemi; Adeyemo, Adesesan Barnabas; Papadaki, Maria; Kotzé, Eduan; university of Plymouth; University of Ibadan, Ibadan, Nigeria; University of the Free State, Bloemfontein, South Africa (Emerald, 2021-06-28)
      Collaborative-based national cybersecurity incident management benefits from the huge size of incident information, large-scale information security devices and aggregation of security skills. However, no existing collaborative approach has been able to cater for multiple regulators, divergent incident views and incident reputation trust issues that national cybersecurity incident management presents. This paper aims to propose a collaborative approach to handle these issues cost-effectively. A collaborative-based national cybersecurity incident management architecture based on ITU-T X.1056 security incident management framework is proposed. It is composed of the cooperative regulatory unit with cooperative and third-party management strategies and an execution unit, with incident handling and response strategies. Novel collaborative incident prioritization and mitigation planning models that are fit for incident handling in national cybersecurity incident management are proposed. Use case depicting how the collaborative-based national cybersecurity incident management would function within a typical information and communication technology ecosystem is illustrated. The proposed collaborative approach is evaluated based on the performances of an experimental cyber-incident management system against two multistage attack scenarios. The results show that the proposed approach is more reliable compared to the existing ones based on descriptive statistics. The approach produces better incident impact scores and rankings than standard tools. The approach reduces the total response costs by 8.33% and false positive rate by 97.20% for the first attack scenario, while it reduces the total response costs by 26.67% and false positive rate by 78.83% for the second attack scenario.
    • Internet of Planets (IoP): A New Era of the Internet

      Kang, Byungseok; Malute, Francis; Bagdasar, Ovidiu; Hong, Choongseon; University of Derby; Kyung Hee University, Seoul, South Korea (Institute of Electrical and Electronics Engineers (IEEE), 2021-06-24)
      Internet of Planets (IoP) is a concept that enables solar planets to communicate with each other using the Internet. While there is a plethora of research on IoP, the delay tolerant network (DTN) has emerged as the most advanced technology in recent years. DTN is an asynchronous networking technology that has been deployed for the networking environment in which steady communication paths are not available, and therefore, it stores receiving data in a data storage and forward them only when the communication links are established. DTN can be applied to sensor networks and the mobile ad-hoc network, as well as space communication that supports data transmissions among satellites. In DTN networking environments, it is crucial to secure a scheme that has relatively low routing overhead and high reliability to achieve efficiency. Thus, this article proposes a time (delay) information based DTN routing scheme, which is able to predict routing paths for achieving efficient data transmissions among the nodes that have comparatively periodic moving patterns. The results of the proposed DTN routing algorithm using NS-3 simulation tools indicate satisfied levels of routing performance in comparison with the existing DTN algorithm.
    • Research on Action Strategies and Simulations of DRL and MCTS-based Intelligent Round Game

      Sun, Yuxiang; Yuan, Bo; Zhang, Yongliang; Zheng, Wanwen; Xia, Qingfeng; Tang, Bojian; Zhou, Xianzhong; Nanjing University, China; University of Derby; Army Engineering University, Nanjing, China (Springer Science and Business Media LLC, 2021-06-16)
      The reinforcement learning problem of complex action control in multiplayer online battlefield games has brought considerable interest in the deep learning field. This problem involves more complex states and action spaces than traditional confrontation games, making it difficult to search for any strategy with human-level performance. This paper presents a deep reinforcement learning model to solve this problem from the perspective of game simulations and algorithm implementation. A reverse reinforcement-learning model based on high-level player training data is established to support downstream algorithms. With less training data, the proposed model is converged quicker, and more consistent with the action strategies of high-level players’ decision-making. Then an intelligent deduction algorithm based on DDQN is developed to achieve a better generalization ability under the guidance of a given reward function. At the game simulation level, this paper constructs Monte Carlo Tree Search Intelligent Decision Model for turn-based antagonistic deduction games to generate next-step actions. Furthermore, a prototype game simulator that combines offline with online functions is implemented to verify the performance of proposed model and algorithm. The experiments show that our proposed approach not only has a better reference value to the antagonistic environment using incomplete information, but also accurate and effective in predicting the return value. Moreover, our work provides a theoretical validation platform and testbed for related research on game AI for deductive games.