Research on a reference signal optimisation algorithm for indoor Bluetooth positioning

GPS has a sharp performance decline in terms of accuracy indoors due to the complex building structure. A combined algorithm, targeting at received signal strength indication (RSSI) calibration optimisation, depending on deep neural network training via input vector Γ and the target output vector Ψ, termed reference signal optimisation algorithm (RSOA) is proposed to improve the positioning accuracy in the indoor Bluetooth positioning networks. Experimental results show that the relative error of the proposed RSOA between the estimated results and the measured ones can reach as low as 0.2%, and the absolute errors can be reduced to 0.13 m at most within 10 m.

Iterative Learning Control for AGV Drive Motor Based on Linear Extended State Observer

In order to solve the problems of repetitive and non-repetitive interference in the workflow of Automated Guided Vehicle (AGV), Iterative Learning Control (ILC) combined with linear extended state observer (LESO) is utilized to improve the control accuracy of AGV drive motor. Considering the working conditions of AGV, the load characteristics of the drive motor are analyzed with which the mathematical model of motor system is established. Then the third-order extended state space equations of the system approximate model is obtained, in which LESO is designed to estimate the system states and the total disturbance. For the repeatability of AGV workflow, ILC is designed to improve the control accuracy. As the goods mass transported each time is not same, the LESO is utilized to estimate the non-repetitive load disturbance in real time and compensate the disturbance of the system to improve the position precision. The convergence of the combined algorithm is also verified. Simulation and experimental results show that the proposed iterative learning control strategy based on LESO can reduce the positioning error in AGV workflow and improve the system performance.

Connected coverage with rapid forwarding in energy harvesting wireless sensor networks for critical rare events

<p>To ensure event detection and subsequent rapid forwarding of notification messages, wireless sensor networks deployed to detect critically important rarely occurring events must maintain both sensing coverage and low latency network connectivity at all times. Maintaining coverage for extended periods is relatively straight forward as passive sensing components tend to consume little energy. Maintenance of network connectivity, however, requires sensing devices constantly supply power to their transceivers, significantly reducing the longevity of the sensor network. Energy harvesting can extend the operational life of sensing devices with always on transceivers, potentially to the point where they can operate year round. In addition, over populating the sensing area with more devices than are required to provide complete sensing cover introduces the possibility of self-organisation where sensing devices agree amongst themselves which will remain active and which will be allowed to sleep. Few algorithms have been proposed to address both coverage and forwarding; those that do are either unconcerned with rapid propagation or have not been optimised to handle the constant changes in topology observed in duty cycling networks. This thesis first analyses the energy consumption profiles of commercially available wireless sensing devices then presents mechanisms by which these devices can both maintain sensing coverage and rapidly forward event detection messages delayed only by the inherent latencies found in wireless multi-hop networks. These individual contributions form the basis of a combined algorithm for Coverage Preservation with Rapid Forwarding (CPRF). Through evaluations including live deployment, CPRF is shown to deliver perfect coverage maintenance and low latency message propagation whilst allowing stored-charge conservation via collaborative duty cycling in energy harvesting networks.</p>

Connected coverage with rapid forwarding in energy harvesting wireless sensor networks for critical rare events

<p>To ensure event detection and subsequent rapid forwarding of notification messages, wireless sensor networks deployed to detect critically important rarely occurring events must maintain both sensing coverage and low latency network connectivity at all times. Maintaining coverage for extended periods is relatively straight forward as passive sensing components tend to consume little energy. Maintenance of network connectivity, however, requires sensing devices constantly supply power to their transceivers, significantly reducing the longevity of the sensor network. Energy harvesting can extend the operational life of sensing devices with always on transceivers, potentially to the point where they can operate year round. In addition, over populating the sensing area with more devices than are required to provide complete sensing cover introduces the possibility of self-organisation where sensing devices agree amongst themselves which will remain active and which will be allowed to sleep. Few algorithms have been proposed to address both coverage and forwarding; those that do are either unconcerned with rapid propagation or have not been optimised to handle the constant changes in topology observed in duty cycling networks. This thesis first analyses the energy consumption profiles of commercially available wireless sensing devices then presents mechanisms by which these devices can both maintain sensing coverage and rapidly forward event detection messages delayed only by the inherent latencies found in wireless multi-hop networks. These individual contributions form the basis of a combined algorithm for Coverage Preservation with Rapid Forwarding (CPRF). Through evaluations including live deployment, CPRF is shown to deliver perfect coverage maintenance and low latency message propagation whilst allowing stored-charge conservation via collaborative duty cycling in energy harvesting networks.</p>

Optimal Sizing of Battery Energy Storage Systems Considering Cooperative Operation with Microgrid Components

Battery energy storage systems (BESSs) are key components in efficiently managing the electric power supply and demand in microgrids. However, the BESSs have issues in their investment costs and operating lifetime, and thus, the optimal sizing of the BESSs is one of the crucial requirements in design and management of the microgrids. This paper presents a problem framework and its solution method that calculates the optimal size of the BESSs in a microgrid, considering their cooperative operations with the other components. The proposed framework is formulated as a bi-level optimization problem; however, based on the Karush–Kuhn–Tucker approach, it is regarded as a type of operation scheduling problem. As a result, the techniques developed for determining the operation schedule become applicable. In this paper, a combined algorithm of binary particle swarm optimization and quadratic programming is selected as the basis of the solution method. The validity of the authors’ proposal is verified through numerical simulations and discussion of their results.

217 Cytotoxicity of nicotinamide enhanced natural killer cells GDA201 is based on metabolic modulation as demonstrated by artificial intelligence assisted analysis of NK cell transcriptome and metabolome

BackgroundNicotinamide (NAM), an allosteric inhibitor of NAD-dependent enzymes, has been shown to preserve cell function and prevent differentiation in ex vivo cell culture. GDA-201 is an investigational natural killer (NK) cell immunotherapy derived from allogeneic donors and expanded using IL-15 and NAM. In previous preclinical studies, NAM led to increased homing and cytotoxicity, preserved proliferation, and enhanced tumor reduction of NK cells. In a phase I clinical trial, treatment with GDA-201 showed tolerability and clinical responses in patients with refractory non-Hodgkin lymphoma (NHL) (Bachanova, et. al., Blood 134:777, 2019). While NAM is known to affect cellular metabolism and participate in 510 enzymatic reactions −in 66 as an inhibitor or activator− its mechanism of action and role in GDA-201 cytotoxicity is unknown.MethodsIn order to define the network of intracellular interactions that leads to the GDA-201 phenotype, flow-cytometry, next generation sequencing (NGS), and liquid chromatography–mass spectrometry (LC-MS)-based metabolite quantification were performed on NK cells cultured for 14 days with IL-15 and human serum in the presence or absence of NAM (7 mM). Artificial Intelligence (AI) machine learning analysis was applied by Pomicell in order to analyze the data using the Pomicell databases supporting data extracted from multiple origins including scientific articles organized using natural language processing tools. AI training was done using a combined algorithm designed to blindly explain and predict the transcriptomic and metabolomic (omics) profile.ResultsOmics analyses defined 1,204 differentially expressed genes, and 100 significantly modified metabolites in the presence of NAM. An in silico model was created that successfully predicted the experimental data in 83% of the cases. Upregulation of TIM-3 expression in GDA-201 was predicted to be mediated by inhibition of IL-10 and SIRT3, via CREB1/HLA-G signaling and adrenoceptor beta 2 (ADRB2) upregulation. Adenosine metabolite reduction supports this and suggests dopaminergic activation of NK cytotoxicity. Upregulation of CD62L in the presence of NAM was predicted to be mediated by transcription factor Dp-1 (TFDP1) via dihydrofolate reductase (DHFR) activation and intracellular folic acid reduction. Interferon-gamma and CASP3 modulation (via JUN and MCL1, respectively), via PPARa inhibition, support that finding.ConclusionsIn conclusion, AI machine learning of transcriptome and metabolome data revealed multiple pleiotropic metabolic pathways modulated by NAM. These data serve to further elucidate the mechanism by which NAM enhances cell function, leading to the observed cytotoxicity and potency of GDA-201.Ethics ApprovalWe hereby declare that the collection of the Apheresis units in the three participating institutes (sites) has been done under an approved clinical study that meets the following requirements:1. Ethics approval has been obtained from the local EC at each of the sites, prior to any study related activities.2. The working procedures of the EC at the sites for conduct of clinical studies are in due compliance with local regulations (Israeli Ministry of Health) and provisions of Harmonized International Guidelines for Good Clinical Practice, namely: ICH-GCP.3. Sites follow EC conditions & requirements in terms of submissions, notifications, and approval renewals. 4. Participants gave Informed Consent (approved by the EC) before taking part in the study.5. Informed Consent has been approved by the ECs. The Israeli template of Informed Consent is in used and it includes study specific information (e.g. study goal, design, method, duration, risks, etc.). Name of the Institute Name of the EC/IRB EC Study No.Hadassah Medical Center Helsinki Committee 0483-16-HMORambam Health Care Campus Helsinki Committee 0641-18-RMBIchilov Sourasky Medical Center Tel-Aviv Helsinki Committee 0025-17-TLV

Application of ALO-ELM in Load Forecasting Based on Big Data

The load of power system changes with the development of economy, short-term load forecasting play a very important role in dispatching and management of power system. In this paper, the Ant Lion Optimizer (ALO) is introduced to improve the input weights and hidden-layer Matrix of extreme learning machine (ELM), after the parameters of ELM are optimized by ALO, then input nodes, hidden layer nodes and output nodes are determined, so a load forecasting model based on ALO-ELM combined algorithm is established. The proposed method is illustrated based on the historical load data of a city in China. The results show that the average absolute error of short-term load demand predicted by ALO-ELM model is 1.41, while that predicted by ELM is 4.34, the proposed ALO-ELM algorithm is superior to the ELM and meet the requirements of engineering accuracy, which proves the effectiveness of proposed method.

ZIGBEE SIGNALS ANALYZER

Широкое распространение беспроводных технологий требует развития средств контроля за устройствами и сетями передачи данных и, в частности, за беспроводными персональными сетями стандарта ZigBee. Известные способы поиска и приема сигналов ZigBee, требующие осуществления предварительной оценки частотного рассогласования, обладают высокой вычислительной сложностью. Некогерентный способ приема сигналов ZigBee не требует больших вычислительных ресурсов, но не обеспечивает удовлетворительную помехоустойчивость. Целью работы являлась разработка комбинированного алгоритма обнаружения и приема сигналов ZigBee. На основе разработанного алгоритма построен анализатор, позволяющий идентифицировать персональные сети, их передающее и приемное устройства. Новизна: для приёма сигналов при неизвестной частотной расстройке используется сочетание когерентной обработки на коротких временных интервалах с их последующим некогерентным накоплением. Предложенный алгоритм способен эффективно работать в неблагоприятных условиях приема и обладает относительно невысокой вычислительной сложностью. Результат: использование представленного решения позволяет выполнять обнаружение и прием сигналов ZigBee радиодоступных источников, идентифицировать персональную сеть, передающее и приемное устройства в этой сети. Практическая значимость: предложенный алгоритм может использоваться для построения анализатора сигналов ZigBee на программно-определяемом радиоприемном устройстве с полосой одновременной обработки сигналов от 2 МГц. Реализованный в универсальных цифровых радиоприемных устройствах семейства АРГАМАК алгоритм применяется в системах поиска и локализации несанкционированных источников радиоизлучений в контролируемых объектах The widespread adoption of wireless technologies requires the development of controls over devices and data networks and in particular over ZigBee wireless personal networks. Known methods of searching for and receiving ZigBee signals, which require a preliminary assessment of frequency offset, have a high computational complexity. The non-coherent method of receiving ZigBee signals does not require large computing resources but does not provide satisfactory noise immunity. The purpose of the work was to develop a combined algorithm for detecting and receiving ZigBee signals. Based on the developed algorithm, we built an analyzer that allows you to identify personal networks, their transmitting and receiving devices. Novelty: to receive signals with an unknown frequency offset, we used a combination of coherent processing at short time intervals with their subsequent non coherent accumulation. The proposed algorithm is able to work effectively in unfavorable reception conditions and has a relatively low computational complexity. Result: the use of the presented solution allows you to detect and receive ZigBee signals from radio-accessible sources, identify a personal network, a transmitting and receiving device in this network Practical relevance: the proposed method can be used to build a ZigBee signal analyzer on an SDR with a band of simultaneous signal processing from 2 MHz. The ZigBee network analyzer, implemented on the basis of a digital radio receiver of the ARGAMAK family, serves as the basis for the device for searching and localizing unauthorized radio sources in controlled objects

H∞–Adaptive H∞ Algorithm-Based State of Charge Estimation Considering the Hysteresis Effect for Lithium Polymer Battery

Accurate state of charge (SoC) estimation is crucial for the safe and reliable running of lithium-ion batteries in electrified transportation equipment. To enhance the estimation accuracy and robustness under different ambient temperatures, H∞ and the adaptive H∞ filterings were first combined to simultaneously forecast the parameters and SoC of the battery model considering the hysteresis effect in this paper. To drop the computational complexity to the most extent, the hysteresis unit was integrated into the first-order RC battery model and the aforementioned combined algorithm was developed under a dual-time frame. Then, the battery model with the hysteresis effect is evaluated against the model without that in terms of the estimation accuracy. Subsequently, the proposed algorithm is compared with the dual H∞ algorithm based on the employed battery model. The results demonstrate the excellent performance of the utilized battery model and the proposed algorithm in terms of both the estimation accuracy and the convergence speed.

Energy-Saving D2D Wireless Networking Based on ACO and AIA Fusion Algorithm

Lower energy consumption and higher data rate have been becoming the key factors of modern wireless mobile communication for the improvement of user experiences. At present, the commercialization of 5G communications is gradually promoting the development of Internet of things (IoT) techniques. Due to the limited coverage capability of direct wireless communications, the indirect device-to-device (D2D) communications using information relay, in addition to the single 5G base station deployment, have been introduced. Along with the increase of information nodes, the relay devices have to undertake the nonnegligible extra data traffic. In order to adjust and optimize the information routing in D2D services, we present an algorithmic investigation referring to the ant colony optimization (ACO) algorithm and the artificial immune algorithm (AIA). By analyzing the characteristics of these algorithms, we propose a combined algorithm that enables the improved the iterative convergence speed and the calculation robustness of routing path determination. Meanwhile, the D2D optimization pursuing energy saving is numerically demonstrated to be improved than the original algorithms. Based on the simulation results under a typical architecture of 5G cellular network including various information nodes (devices), we show that the algorithmic optimization of D2D routing is potentially valid for the realization of primitive wireless IoT networks.