An enhanced localisation system for indoor environment using clustering technique

Power systems have been undergoing radical changes in recent years, and their planning and operation will be surely undertaken according to the Smart Grid (SG) vision in the near future. The SG initiatives aim at introducing new technologies and services in power systems, to make the electrical networks more reliable, efficient, secure and environmentally-friendly. In particular, it is expected that communication technologies, computational intelligence and distributed energy sources will be widely used for the whole power system in an integrated fashion. In particular, nowadays, unprecedented challenges like as stringent regulations, environmental concerns, growing demand for high quality, reliable electricity and rising customer expectations are forcing utilities to rethink about electricity generation and delivery from the bottom up. Moreover, the availability of low cost computing and telecommunications technologies, new generation options, and scalable, modular automation systems push utilities to be dynamic, innovative and ambitious enough to take advantage of them. Driven by the dynamics of the new energy environment, leading utilities, technology vendors and government organizations have created a vision of the next generation of energy delivery systems: the Smart Grid. Operational changes of the grid, caused by restructuring of the electric utility industry and electricity storage technology advancements, have created an opportunity for storage systems to provide unique services to the evolving grid. Especially Battery Energy Storage Systems (BESSs), thanks to the large number and variety of services they can provide, are powerful tools for the solution of some challenges that future grids will face. This consideration makes BESSs critical components of the future grids. The BESS can be applied for different services into the different levels of power system chain to satisfy technical challenges and provide financial benefits. In the context of the application of BESSs in SGs, there are two main problems that need to be addressed in a way that exploits the BESS potential, that are linked to their operation and sizing. This thesis focuses on both these aspects, proposing new strategies that allow optimizing the BESS adoption. When dealing with BESSs, sizing and operation are strictly linked. The correct sizing of a BESS, in fact, needs to take into account its operation which in turn will be effected with the aim of optimizing the whole system where it is included. In the first part of this research study, advanced optimal operating strategies were proposed for BESSs by considering both the distribution system operator perspective and the end user. Thus, the proposed operating strategies were performed with the aim of (i) leveling the active power requested by the loads connected to a distribution system (distribution system operator service), (ii) reducing the electricity costs sustained by an end-use costumer that provides demand response (DR) (end user service) and (iii) scheduling a microgrid (µG) with DR resources such as Plug-in Electric Vehicles (PEVs) and Data Centers (DCs) (both the two section service). The proposed strategies also satisfied technical constraints of BESSs and other components of the µG. The second part of the thesis presented the optimal sizing of BESSs aimed at maximizing the benefits related to their use. In the thesis, the sizing, which is performed by considering the end user point of view with reference to both the industrial and residential customers, is effected by adopting both deterministic and probabilistic approaches. With reference to the deterministic approach, a simple and quick closed form procedure for the sizing of BESSs in residential and industrial applications was proposed. In case of probabilistic approach, the case of a BESS installed in an industrial facility was considered and the sizing was performed based on the decision theory. Technical improvements and economic benefits of optimal operation and optimal sizing of BESSs in SG are demonstrated by the obtained results which are reported in the numerical applications. More specifically, it was clearly determined that BESSs can offer technical supports into the distribution operator section of the grid in terms of load management and security challenges. Moreover optimal integration of BESSs into the grid was also appealing for end users thanks to valuable amounts of electricity bill cost reduction. Regarding the original contribution of the thesis, the following considerations can be done. With reference to the load leveling service, an innovative two-step procedure (day-ahead scheduling and very short time predictive control) was proposed which optimally controls a BESS connected to a distribution substation in order to perform load leveling. In case of DR, a proper control of the BESS was proposed in order to perform DR under different price schemes, such as Real Time Pricing (RTP) and Time of Use (TOU) without modifying the daily work cycle of the industrial loads. The control procedure allows achieving contemporaneously two important goals that are the reduction of the bill costs and the prolonging the battery's lifetime so further reducing the costs sustained by the customer. With reference to the scheduling of microgrids, the original contribution of the thesis is focused on the proposal of optimization strategies aimed at managing and coordinating, simultaneously, batteries on board of vehicles or equipping data centers' Uninterruptable Power Supply (UPS) and Distributed Generation (DG) units. Also comparisons among different single-objective based strategies are made in order to highlight the most convenient. With reference to the sizing based on deterministic approach, unlike the other relating literature, the innovative contribution is that the closed form procedure takes into account both the technical constraints of the battery and contractual agreements between the customer and the utility. Moreover, in the economical analysis performed for the sizing, which is applied with reference to both residential and small industrial customers and is based on actual TOU tariffs, a wide sensitivity analysis to consider different perspectives in terms of life span and future costs was performed. Some aspects that affect the profitability of the battery, such as technological limitations (e.g. the battery and converter efficiency), economic barriers (e.g. capital cost and the rate of change of the cost) and variation of the load profile along the years were deeply analyzed. In case of sizing based on probabilistic approach, the original contributions of the thesis are mainly referred to the proposal of a new method that uses a decision theory-based process to obtain the best sizing alternative considering the various uncertainties affecting the sizing procedure. The thesis is organized in three chapters which are dealing with integration of BESSs in SGs. The first chapter reports basic concepts and characteristics of BESSs, fundamental components and features of SGs and different services that BESSs can provide. The optimal operation strategies of BESS are considered in second chapter which includes their problem formulation, solving procedures and results. The third chapter deals with the optimal sizing problem of BESSs for which the problem formulation, solving procedures and results are reported. Finally, the conclusions are presented in the last part of thesis.

The most commonly used deterministic approach to the development of total maximum daily loads (TMDLs) fails to explicitly address issues related to a margin of safety and inherent variability of streamflows in the process of TMDL development. In this paper, the deterministic approach to pH TMDL development for Beech Creek watershed, Muhlenberg County, Kentucky, proposed by Ormsbee, Elshorbagy and Zechman is discussed. The shortcomings and the limitations of the assumptions associated with the deterministic approach are highlighted. An alternative probabilistic approach, to cope with the percentile-based water quality standards based on Monte Carlo simulation, is presented and compared to the deterministic approach. The proposed probabilistic approach provides a deeper insight into the issue of uncertainty and emphasizes the importance of handling the water quality standards and TMDLs in terms of magnitude and frequency rather than a single-valued approach. Expected exceedances and the confidence of compliance with percentile-based standards are estimated. Accordingly, an objective method of estimating the margin of safety for pH TMDLs is proposed.

A radio frequency (RF) based device-free indoor localization (DFL) has attracted a lot of research effort due to its simplicity, less costly and compatibility with the existing hardware equipped with RF interface compared to the existing positioning system. An advanced monitoring system, known as Ambient Assisted Living (AAL) has been developed using DFL and Internet of Things (IoT) technologies. In this paper, we present a probabilistic DFL system using passive radio map method based on a non-parametric histogram-based approach to locate and map the passive target position in an indoor area. The proposed technique is based on a radio map concept in locating human position using received signal strength indicator (RSSI). The Bayesian inversion was introduced in the proposed approach for estimating the density function and the Probability of Error metric (PoE) was used to evaluate the tracking accuracy of the system. We firstly performed system analysis on the deterministic approach for comparison with the proposed probability approach. The results show that the probabilistic approach can accurately locate a passive target with an error probability of 0.0782 compared to the deterministic approach that gives high PoE of 0.2463.

Wi-Fi ingerprint-based localization is currently the most promising approach to building metropolitan-scale localization systems for both indoor and outdoor environments. A Wi-Fi fingerprint localization system makes use of a radio map that contains Wi-Fi signals received at various locations, and uses the map for location estimation. Thus, the accuracy of a Wi-Fi fingerprint localization system greatly depends on the quality of its radio map. This paper explores various properties of a metropolitan radio map that may affect the accuracy of Wi-Fi fingerprint localization systems. In this study, metropolitan-scale radio maps are obtained using war walking and war driving. These maps contain hundreds of thousands of access points and signal samples. A detailed comparison analysis of selected radio map properties reveals how different map properties affect the difference between the positional accuracies of the driving and walking radio maps in Wi-Fi fingerprint-based localization.

This paper presents a self-optimal clustering (SOC) technique which is an advanced version of improved mountain clustering (IMC) technique. The proposed clustering technique is equipped with major changes and modifications in its previous versions of algorithm. SOC is compared with some of the widely used clustering techniques such as K-means, fuzzy C-means, Expectation and Maximization, and K-medoid. Also, the comparison of the proposed technique is shown with IMC and its last updated version. The quantitative and qualitative performances of all these well-known clustering techniques are presented and compared with the aid of case studies and examples on various benchmarked validation indices. SOC has been evaluated via cluster compactness within itself and separation with other clusters. The optimizing factor in the threshold function is computed via interpolation and found to be effective in forming better quality clusters as verified by visual assessment and various standard validation indices like the global silhouette index, partition index, separation index, and Dunn index.

This study aims at making the relationship between the deterministic approach and the probabilistic approach in the structural earthquake response analysis clear by graphically depicting the constraint curves for responses. The present aseismic design of a structure is carried out by deterministic approach using the response spectrum and modal analysis. As most structures face random external forces, however, it is necessary to introduce the probabilistic concept into the aseismic design.In this study, the authors have formulated the structural earthquake response analysis by two approaches: one is the deterministic approach using the response spectrum and modal analysis, while the other is the probabilistic approach using the power spectrum density, random vibration theory and dynamic reliability analysis.Some structures of multi-degrees-of-freedom were analyzed by the two approaches, and the constraint curves for these responses were graphically illustrated. From these results, it is shown that the constraint curves determined by the deterministic approach are quite similar to those determined by the probabilistic approach. A quantitative relationship between the two responses has been obtained by evaluating these constraint curves.

Between 2011 and 2022 greater than 50% of global E&P merger and acquisition (M&A) transactions destroyed shareholder value with only 25% generating approximately 60% of total excess returns, per a 2024 paper from McKinsey & Co.1 Upgrading the financial analysis approach to provide better insights for M&A decision-making professionals is therefore increasingly important to enhance their probability of success. This paper analyzes the 2021 merger of Cabot Oil & Gas with Cimarex Energy (the Transaction) to understand the distinct advantages gained from applying a probabilistic approach to the financial analysis of mergers, acquisitions and divestitures. Traditional deterministic valuation and transaction analyses, which rely on a static set of assumptions, are compared to a probabilistic approach based on Monte Carlo simulation using a readily available Excel add-in. This paper reviews and utilizes the additional insights from the probabilistic output, generated from the input probability distribution functions and correlation coefficients chosen, unavailable from a traditional, deterministic financial approach. The probabilistic approach provides an enhanced transaction analysis process that produces the critical metrics company Boards and shareholders increasingly require in determining if a transaction should be undertaken. This paper highlights how a probabilistic approach to the M&A process: (i) is straightforward to implement; (ii) facilitates a greater use of real-world data; (iii) generates outcome probabilities for key transaction metrics unavailable with a deterministic approach; and (iv) provides unique risk insights into those transaction metrics. This probabilistic approach to the M&A process represents a significant, practical improvement to management's ability to evaluate specific transactions and enhance their likelihood of success.

Human health risks associated with dietary exposure to persistent organic pollutants (POPs) in river water in Korea

Different methodologies for flood-plain mapping are analysed and discussed by comparing deterministic and probabilistic approaches using hydrodynamic numerical solutions. In order to facilitate the critical discussion, state-of-art techniques in the field of flood inundation modelling are applied to a specific test site (River Dee, UK). Specifically, different flood-plain maps are derived for this test site. A first map is built by applying an advanced deterministic approach: use of a fully two-dimensional finite element model (TELEMAC-2D), calibrated against a historical flood extent, to derive a 1-in-100 year flood inundation map. A second map is derived by using a probabilistic approach: use of a simple raster-based inundation model (LISFLOOD-FP) to derive an uncertain flood extent map predicting the 1-in-100 year event conditioned on the extent of the 2006 flood. The flood-plain maps are then compared and the advantages and disadvantages of the two different approaches are critically discussed. Citation Di Baldassarre, G., Schumann, G., Bates, P. D., Freer, J. E. & Beven, K. J. (2010) Flood-plain mapping: a critical discussion of deterministic and probabilistic approaches. Hydrol. Sci. J. 55(3), 364–376.

Location fingerprinting provides localization for devices in indoor environments using existing Wireless Local Area Network (WLAN) infrastructure. However, the initial offline calibration which is required for these types of systems is non-trivial and requires significant labour cost. The current practice is to collect measurements at calibration points in a uniform grid for the area which is covered by the radio map. However, not all calibration points are resolvable in signal space and addition of unresolvable calibration points does not improve the localization accuracy. This paper presents Spatial Aware Signal Space Clustering (S3C) clustering algorithm which analyses walk test data for identifying these unresolvable calibration points prior to calibration phase. Simulation based studies shows that the algorithm is able to reduce the labour cost of calibration phase while preserving the localization accuracy.

One of the problems associated with the implementation of indoor location detection systems is the time-consuming procedure of equipment adjustment, which includes indoor map construction, radio signal map creation and calibrating signal propagation model. Thus, the equipment adjustment is a time-consuming and expensive process to be perform every time when there are changes in equipment configuration and allocation. The developed indoor localization system provides navigation of the user inside a room and allows to building radio map and putting Bluetooth Low Energy (BLE) beacons on the map of a room by the efforts of a number of users walking indoors. The architecture of the system is developed so that the different indoor localization techniques can be used and different services can be requested by the user’s mobile application. The user’s navigation inside the room is a combination of PDR based on the built-in smartphone sensors, multilateration and fingerprinting. The indoor navigation ontology is implemented to make decision which of these methods should be used. The key feature of the system is determining the location of BLE beacon. For this purpose the Random Forest algorithm is used, which uses signal levels, user rotation angles and distance to Bluetooth beacon as a training dataset. The geometric parameters of a room are estimated by the radio map and Bluetooth beacon locations.

This paper presents the seismic hazard analysis of Jaitapur region on the west coast of India, where a nuclear power plant is being planned to locate. A homogenized earthquake catalog in unified moment magnitude scale for the region has been prepared compiling updated data from different agencies. The seismicity parameters were estimated for Jaitapur region, and the hazard evaluation has been performed using different methodologies with different source models and attenuation relations. The deterministic and probabilistic seismic hazard analyses were performed with currently available data and their best possible scientific interpretation by considering alternative models (source models, maximum magnitude, and attenuation relationships). The hazard maps have been produced for horizontal ground motion on the bedrock level using both deterministic and probabilistic methods. The value of Peak Horizontal Acceleration (PHA) for the study region estimated probabilistically for 475 year return period varies from 0.05 to 0.2 g, whereas with deterministic approach, PHA reaches value as high as 0.3 g.KeywordsFirst keywordSecond keywordThird keyword

Prestressed concrete poles nowadays are widely used in supporting the catenary cables of train systems. Compared to their importance to the functionality of the train system, this type of structures have not yet received adequate attention from researchers. We have started tracing the changes in the dynamic behavior of these poles caused by the train passing and the degradation of the materials over a long-time period. In this aim, we installed a structural monitoring system on three of them along one of the high-speed train tracks in Germany. The efficient analysis of the recorded measurements by this system requires a well-known data covering the real material properties of the given structures considering uncertainties of the different parameters. In this paper, we inversely identify the material properties of the poles using deterministic and probabilistic approaches based on the experimental measurements of a full-scale structure and Finite Elements Models. In the deterministic approach, the parameters are identified using the simplex optimization algorithm. Uncertainty of the identified parameters is quantified using a Markov Estimator. In the probabilistic approach, Bayesian inference is utilized for better estimation of the probability distribution of the parameters. Both approaches are suitable for the estimation of mean values of the parameters. The Bayesian method, even though computationally more demanding, is additionally suitable for determining the probability distributions and quantifying the uncertainties of the identified parameters and the correlations between each pair of them. The results show the efficiency of each approach to identify the parameters of the poles. For a rough estimation of the mean values, we recommend the deterministic approach as a simple tool. Conversely, the Bayesian approach is recommended for more detailed and accurate estimation.

The inverse problem of gravimetry under uncertainty of heterogeneous a priori information is solved. An algorithm using the possibilities of deterministic and probabilistic approaches is developed. In the framework of the probabilistic approach, a priori distribution of model parameters described by fuzzy sets. A deterministic approach is used to calculate fields from a given distribution of model parameters and formalization of a priori information through natural restrictions. Since the establishment of this algorithm is independent, it can be used for solving a wide range of nonlinear geophysical inverse problems.

Broadcast operation, which disseminates information network-wide, is very important in multi-hop wireless networks. Due to the broadcast nature of wireless media, not all nodes need to transmit in order for the message to reach every node. Previous work on broadcast support can be classified as probabilistic (each node rebroadcasts a packet with a given probability) or deterministic approaches (nodes pre-select a few neighbors for rebroadcasting). In this paper, we show how network-coding can be applied to a deterministic broadcast approaches, resulting in significant reductions in the number of transmissions in the network. We propose two algorithms, that rely only on local two-hop topology information and makes extensive use of opportunistic listening to reduce the number of transmissions: 1) a simple XOR-based coding algorithm that provides up to 45% gains compared to a non-coding approach and 2) a Reed-Solomon based coding algorithm that determines the optimal coding gain achievable for a coding algorithm that relies only on local information, with gains up to 61% in our simulations. We also show that our coding-based deterministic approach outperforms the coding-based probabilistic approach presented in (C. Fragouli et al, 2006).