Utilizing an Adaptive Neuro-Fuzzy Inference System (ANFIS) for overcrowding level risk assessment in railway stations

The railway network plays a significant role (both economically and socially) in assisting the reduction of urban traffic congestion. It also accelerates the decarbonization in cities, societies and built environments. To ensure the safe and secure operation of stations and capture the real-time risk status, it is imperative to consider a dynamic and smart method for managing risk factors in stations. In this research, a framework to develop an intelligent system for managing risk is suggested. The adaptive neuro-fuzzy inference system (ANFIS) is proposed as a powerful, intelligently selected model to improve risk management and manage uncertainties in risk variables. The objective of this study is twofold. First, we review current methods applied to predict the risk level in the flow. Second, we develop smart risk assessment and management measures (or indicators) to improve our understanding of the safety of railway stations in real-time. Two parameters are selected as input for the risk level relating to overcrowding: the transfer efficiency and retention rate of the platform. This study is the world’s first to establish the hybrid artificial intelligence (AI) model, which has the potency to manage risk uncertainties and learns through artificial neural networks (ANNs) by integrated training processes. The prediction result shows very high accuracy in predicting the risk level performance, and proves the AI model capabilities to learn, to make predictions, and to capture risk level values in real time. Such risk information is extremely critical for decision making processes in managing safety and risks, especially when uncertain disruptions incur (e.g., COVID-19, disasters, etc.). The novel insights stemmed from this study will lead to more effective and efficient risk management for single and clustered railway station facilities towards safer, smarter, and more resilient transportation systems

Fuzzy Logic in Decision Support: Methods, Applications and Future Trends

During the last decades, the art and science of fuzzy logic have witnessed significant developments and have found applications in many active areas, such as pattern recognition, classification, control systems, etc. A lot of research has demonstrated the ability of fuzzy logic in dealing with vague and uncertain linguistic information. For the purpose of representing human perception, fuzzy logic has been employed as an effective tool in intelligent decision making. Due to the emergence of various studies on fuzzy logic-based decision-making methods, it is necessary to make a comprehensive overview of published papers in this field and their applications. This paper covers a wide range of both theoretical and practical applications of fuzzy logic in decision making. It has been grouped into five parts: to explain the role of fuzzy logic in decision making, we first present some basic ideas underlying different types of fuzzy logic and the structure of the fuzzy logic system. Then, we make a review of evaluation methods, prediction methods, decision support algorithms, group decision-making methods based on fuzzy logic. Applications of these methods are further reviewed. Finally, some challenges and future trends are given from different perspectives. This paper illustrates that the combination of fuzzy logic and decision making method has an extensive research prospect. It can help researchers to identify the frontiers of fuzzy logic in the field of decision making

Analisis Perbandingan Metode Fuzzy Tsukamoto, Mamdani dan Sugeno dalam Pengambilan Keputusan Penentuan Jumlah Distribusi Raskin di Bulog Sub. Divisi Regional (Divre) Cianjur

Beras untuk penduduk miskin (raskin) merupakan salah satu program pemerintah untuk meningkatkan kesejahteraan penduduk miskin yang dikelola oleh Bulog. Permasalahan yang terjadi yaitu sering kurang tepatnya pemberian jumlah raskin dibandingkan dengan jumlah penduduk miskin yang ada di suatu kota/kabupaten diantaranya Kabupaten Cianjur. Salah satu model yang dapat digunakan untuk pengambilan keputusan jumlah distribusi raskin adalah model logika fuzzy karena model ini dapat menangani data-data yang tidak linier. Pada penelitian ini dilakukan perbandingan antara ketiga metode sistem inferensia fuzzy yang sering digunakan yaitu metode Tsukamoto, Mamdani, dan Sugeno. Variabel input berupa jumlah penduduk miskin dan jumlah rata-rata stok, sedangkan variabel output berupa jumlah distribusi raskin. Untuk mengevaluasi output digunakan dua teknik yaitu perbandingan standar eror dan uji hipotesis t-test. Dari ketiga metode diperoleh nilai MAPE masing-masing untuk metode Tsukamoto (28,05%), Mamdani(39,05%), dan Sugeno (7,45%). Untuk uji hipotesis hanya metode Sugeno yang hipotesisnya diterima sehingga dari dua teknik evaluasi output, metode Sugeno yang terbaik untuk pengambilan keputusan jumlah distribusi raskin di Cianju

Fiber Optic Attenuation Analysis Based on Mamdani Fuzzy Logic in Gambir Area, Central Jakarta

In this study, the authors conducted an analysis of the quality of fiber optic network maintenance based on attenuation value and maintenance time using fuzzy Mamdani logic and simulated using Matlab software, to improve accuracy in drawing conclusions on maintaining quality. This study uses a quantitative method, in which the author obtains a summary of customer data from PT. Telkom Indonesia in a period of 4 months of observation from August to November 2021. In August there were 776 customers, in September there were 362 customers, in October there were 359 customers, and in November 445 customers who underwent Indihome fiber optic cable maintenance. The test results with the centroid method with an input Handling Time of 1.5 hours and an Attenuation of 15 dB, then the output Repair Quality is 5.5 or categorized as Good. The greater the attenuation value generated, the more time it takes to maintain the IndiHome internet network disturbance. This is due to the many technical maintenance of fiber optic cables carried out by technicians to adjust for damage/trouble in the field. It is expected that maintenance can be carried out routinely in order to avoid fatal internet disturbances on the customer's side, and maximize maintenance time according to the dosage determined by the company, which is less than 3 hours, taking into account the work performance of technicians and also the quality of maintenance

ORGANIZATION: COMPARISON MAMDANI FUZZY LOGIC vs SUGENO

Developing world-wide economy in an extremely fast rate of economic crisis followed by analysis required orientation at both the macro and micro economic indicators are interdependent in three-dimensional space by viewing reports and unified and combinations of input data to each other. These analyzes currently use concept (concept) of fuzzy logic to describe how close to reality phenomena or processes that are highly unstable. So many factors input is influenced by feedback organizations

An intelligent fuzzy logic-based content and channel aware downlink scheduler for scalable video over OFDMA wireless systems

The recent advancements of wireless technology and applications make downlink scheduling and resource allocations an important research topic. In this paper, we consider the problem of downlink scheduling for multi-user scalable video streaming over OFDMA channels. The video streams are precoded using a scalable video coding (SVC) scheme. We propose a fuzzy logic-based scheduling algorithm, which prioritises the transmission to different users by considering video content, and channel conditions. Furthermore, a novel analytical model and a new performance metric have been developed for the performance analysis of the proposed scheduling algorithm. The obtained results show that the proposed algorithm outperforms the content-blind/channel aware scheduling algorithms with a gain of as much as 19% in terms of the number of supported users. The proposed algorithm allows for a fairer allocation of resources among users across the entire sector coverage, allowing for the enhancement of video quality at edges of the cell while minimising the degradation of users closer to the base station

Entwicklung eines auf Fuzzy-Regeln basierten Expertensystems zur Hochwasservorhersage im mesoskaligen Einzugsgebiet des Oberen Mains

People worldwide are faced with flood events of different magnitudes. A timely and reliable flood forecast is essential for the people to save goods and, more important, lives. The development of a fuzzy rule based flood forecast system considering extreme flood events within meso-scale catchments and with return periods of 100 years and more is the main objective of this work. Considering one river catchment extreme flood events are usually seldom. However, these data are essential for a reliable setup of warning systems. In this work the database is extended by simulations of possible flood events performing the hydrological model WaSiM-ETH (Water balance Simulation model ETH) driven by generated precipitation fields. The therefore required calibration of the hydrological model is performed applying the genetic optimization algorithm SCE (Shuffled Complex Evolution). Thereby, different SCE configuration setups are investigated and an optimization strategy for the Upper Main basin is developed in order to ensure reliable und satisfying calibration results. In this thesis the developed forecast system comprises different time horizons (3 days; 6, 12, and 48 hours) in order to ensure a reliable and continuous flood forecast at the three main gauges of the Upper Main river. Thereby, the focus of the different fuzzy inference systems lies on different discharge conditions, which together ensure a continuous flood forecast. In this work the performance of the two classical fuzzy inference systems, Mamdani and Takagi-Sugeno, is investigated considering all four forecast horizons. Thereby, a wide variety of different input features, among others Tukey data depth, is taken into consideration. For the training of the fuzzy inference systems the SA (Simulated Annealing) optimization algorithm is applied. A further performance comparison is carried out considering the 48 hour forecast behaviour of the two fuzzy inference systems and the hydrological model WaSiM-ETH. In this work the expert system ExpHo-HORIX is developed in order to combine the single, trained fuzzy inference systems to one overall flood warning system. This expert system ensures beside the fast forecast a quantification of uncertainties within a manageable, user-friendly, and transparent framework which can be easily implemented into an exiting environment.Menschen weltweit werden mit Hochwasserereignissen unterschiedlicher Stärke konfrontiert. Um Eigentum und, noch viel wichtiger, Leben zu retten, ist eine rechtzeitige und zuverlässige Hochwasserwarnung und folglich -vorhersage unerlässlich. Ziel dieser Arbeit ist es deshalb, ein auf Fuzzy-Regeln basiertes Hochwasserwarnsystem für mesoskalige Einzugsgebiete und die Vorhersage von extremen Hochwasserereignissen mit Wiederkehrperioden von 100 Jahren und mehr unter Berücksichtigung von Unsicherheiten zu entwickeln. Da extreme Hochwasserereignisse mit einer Jährlichkeit von 100 oder mehr Jahren in der Realität nicht in jedem Einzugsgebiet bereits beobachtet und aufgezeichnet wurden, ist eine Erweiterung der Datenbank auf Grund von Modellsimulationen zwingend notwendig. In dieser Arbeit werden hierzu das hydrologische Modell WaSiM-ETH (Wasserhaushalts-Simulations-Modell ETH) sowie von Bliefernicht et al. (2008) generierte Niederschlagsfelder verwendet. Die Kalibrierung des Modells erfolgt mit dem SCE (Shuffled Complex Evolution) Optimierungsalgorithmus. Um reproduzierbare Kalibrierungsergebnisse zu erzielen und die notwendige Kalibrierungszeit möglichst gering zu halten, werden unterschiedliche Optimierungskonfigurationen untersucht und eine Kalibrierungsstrategie für das mesoskalige Einzugsgebiet des Oberen Mains entwickelt. Um eine kontinuierliche und zuverlässige Vorhersage zu garantieren, ist die Idee entwickelt worden, Fuzzy-Regelsysteme für unterschiedliche Vorhersagehorizonte (3 Tage; 6, 12 und 48 Stunden) für die drei Hauptpegel des Oberen Mains aufzustellen, die im Zusammenspiel eine kontinuierliche Vorhersage sicher stellen. Der Fokus der 3-Tagesvorhersage liegt hierbei in der zuverlässigen Wiedergabe von geringen und mittleren Abflussbedingungen sowie der zuverlässigen und rechtzeitigen Vorhersage von Überschreitungen einer vordefinierten Meldestufe. Eine vorhergesagte Überschreitung der Meldestufe führt zu einem Wechsel der Vorhersagesysteme von der 3-Tages- zu der 6-, 12- und 48-Stundenvorhersage, deren Fokus auf der Vorhersage der Hochwasserganglinie liegt. In diesem Zusammenhang wird die Effizienz der beiden klassischen Regelsysteme,Mamdani und Takagi-Sugeno, sowie die Kombination unterschiedlicher Eingangsgrößen, unter anderem Tukey Tiefenfunktion, näher untersucht. Ein weiterer Effizienzvergleich wird zwischen den Mamdani Regelsystemen der 48-Stundenvorhersage und dem hydrologischen ModellWaSiM-ETH durchgeführt. Für das Training der beiden Regelsysteme wird der SA (Simulated Annealing) Optimierungsalgorithmus verwendet. Die einzelnen Fuzzy-Regelsysteme werden schließlich in dem entwickelten Hochwasserwarnsystem ExpHo-HORIX (Expertensystem Hochwasser - HORIX) zusammengefügt. Standardmäßig wird für jede Vorhersage die Niederschlagsunsicherheit auf Grund von Ensemble-Vorhersagen innerhalb ExpHo-HORIX analysiert und ausgewiesen. Im Hochwasserfall können für die stündlichen Fuzzy-RegelsystemeModellunsicherheiten des hydrologischenModells, das für die Generierung der Datenbank von Extremereignissen verwendet wurde, zusätzlich ausgewiesen werden. Hierzu müssen zusätzlich Ergebnisse der SCEM Analyse (Grundmann, 2009) vorliegen

Quality of Service for Multimedia and Control System Applications in Mobile Ad-hoc Network

A Mobile Ad-Hoc Network (MANET) is a collection of randomly distributed infrastructure-less mobile nodes that form a wireless network. These Mobile nodes have the capability to act as a host or relay. As a host, the mobile nodes can be the source and/or destination of traffic, and when acting as a relay, they can be an intermediate node that forwards the traffic to its destination. Some of the challenges of a MANET include the dynamic network topology, device discovery, power constraints, wireless channel conditions and limited network resources. These challenges degrade the network performance and thus affect the network stability and robustness. Therefore, it is difficult for a MANET to attain the Quality of Service (QoS) of a wired network. This thesis aims to address the problem of the limited wireless network resources by proposing two adaptive scheduling algorithms that can adapt in real-time to the changes in the network. To achieve the aim; this thesis first analyses the behaviour of various application profiles in a queue. It models Voice, Email, and Internet Browsing traffic (by specifying packet sizes, and inter-arrival rates based on various distributions) separately and then simultaneously in a common network for uncongested and congested conditions, after which scheduling is applied in order to improve the overall network performance. The Voice traffic profile is then added to the UDP/IP protocol stack and the network performance is compared to a simple node without the UDP/IP protocol stack. A realistic wireless propagation model for the simulation is developed from a point-to-point open-field outdoor experiment. This thesis proposes two adaptive priority fuzzy based scheduler for a MANET, the priority of packets in the queue are determined based on the real-time available network resources. The methodology for transmitting a live-feed video stream over OPNET to validate the scheduler is also presented. An interface between the simulation and hardware is created to send real-time video traffic through the simulation network. This thesis concludes by showing that the performance of a MANET network can be improved by applying an adaptive scheduler