Health Indicators to Estimate Health of Magnetic Wheel Encoder

Performance of several vehicle safety features, such as anti-lock brake system (ABS), traction control system (TCS) and electronic stability control (ESC) rely on the quality of wheel speed signal. One potential failure mode for the wheel speed encoders is gradual deposition of foreign paramagnetic debris on the surface of the encoder. This results in reduced strength of the magnetic field, and impacts the quality of the wheel speed signal. Noisy wheel speed signal jeopardizes performance of safety critical features, affecting safety, stability, drivability, and negatively impacts customer’s experience. In this paper, several faulty encoders with various levels of faults have been used in data collection in a test bench. A prognostics methodology is proposed to evaluate the magnetic wheel encoder’s health. This method leverages time domain and frequency domain-based health indicators to monitor the deterioration in wheel encoder. Time domain-based health indicators include VDA (Verband der Automobilindustrie) signals that are generated by advanced wheel speed sensors, and an enveloping filter of the wheel speed signal’s noise. Frequency domain-based health indicator include root mean square amplitude of average order spectrum of wheel speed noise. The performance of individual/combination of these health indicators are compared to assess the separation between healthy encoder and degraded encoders. Results indicate that it is possible to monitor the degradation process due to magnetic debris accumulation, using the proposed method.

Mean value estimates of gcd and lcm-sums

We study the distribution of the generalized gcd and lcm functions on average. The generalized gcd function, denoted by [Formula: see text], is the greatest [Formula: see text]th power divisor common to [Formula: see text] and [Formula: see text]. Likewise, the generalized lcm function, denoted by [Formula: see text], is the smallest [Formula: see text]th power multiple common to [Formula: see text] and [Formula: see text]. We derive asymptotic formulas for the average order of the arithmetic, geometric, and harmonic means of [Formula: see text]. Additionally, we also deduce asymptotic formulas with error terms for the means of [Formula: see text], and [Formula: see text] over a set of lattice points, thereby generalizing some of the previous work on gcd and lcm-sum estimates.

On the average order of the gcd-sum function over arbitrary sets of integers

Let \mathbb{N} denote the set of all positive integers and for j,n \in \mathbb{N}, let (j,n) denote their greatest common divisor. For any S\subseteq \mathbb{N}, we define P_{S}(n) to be the sum of those (j,n) \in S, where j \in \{1,2,3, \ldots, n\}. An asymptotic formula for the summatory function of P_{S}(n) is obtained in this paper which is applicable to a variety of sets S. Also the formula given by Bordellès for the summatory function of P_{\mathbb{N}}(n) can be derived from our result. Further, depending on the structure of S, the asymptotic formulae obtained from our theorem give better error terms than those deducible from a theorem of Bordellès (see Remark 4.4).

Retail atmospherics effect on store performance and personalised shopper behaviour: a cognitive computing approach

PurposeThe best possible way for brick-and-mortar retailers to maximise engagement with personalised shoppers is capitalising on intelligent insights. The retailer operates differently with diversified items and services, but influencing retail atmospheric on personalised shoppers, the perception remains the same across industries. Retail atmospherics stimuli such as design, smell and others create behavioural modifications. The purpose of this study is to explore the atmospheric effects on brick-and-mortar store performance and personalised shopper's behaviour using cognitive computing based in-store analytics in the context of emerging market.Design/methodology/approachThe data are collected from 35 shoppers of a brick-and-mortar retailer through questionnaire survey and analysed using quantitative method.FindingsThe result of the analysis reveals month-on-month growth in footfall count (46%), conversation rate (21%), units per transaction (27%), average order value (23%), dwell time (11%), purchase intention (29%), emotional experience (40%) and a month-on-month decline in remorse (20%). The retailers need to focus on three control gates of shopper behaviour: entry, browsing and exit. Attention should be paid to the cognitive computing solution to judge the influence of retail atmospherics on store performance and behaviour of personalised shoppers. Retail atmospherics create the right experience for individual shoppers and forceful use of it has an adverse impact.Originality/valueThe paper focuses on strategic decisions of retailers, the tactical value of personalised shoppers and empirically identifies the retail atmospherics effect on brick-and-mortar store performance and personalised shopper behaviour.

First radio evidence for ubiquitous magnetic reconnections and impulsive heating in the quiet solar corona

<p>It has been a long standing problem as to how the solar corona can maintain its million K temperature, while the photosphere, which is the lowest layer of the solar atmosphere, is only at a temperature of 5800 K. A very promising theory to explain this is the “nanoflare” hypothesis, which suggests that numerous flares of energies ~10<sup>24</sup> ergs are always happening in the solar corona, and maintain its million K temperature. However, detecting these nanoflares directly is challenging with the current instrumentation as they are hypothesised to occur at very small spatial, temporal and energy scales. These nanoflares are expected to produce nonthermal electrons, which are expected to emit in the radio band. These nonthermal emissions are often brighter than their thermal counterparts and might be detectable with current radio instruments. Due to their importance multiple searches for these nonthermal emissions have been done, but thus far they have been  limited to active regions. The quiet corona is also hot, and often comprises the bulk of the coronal region, so it is equally important to understand the physical processes which maintain this medium at MK temperatures. We describe the results from our effort to use the data from the Murchison Widefield Array (MWA) to search for impulsive radio emissions in the quiet solar corona. By pushing the detection threshold of nonthermal emission by about two orders of magnitude lower than previous studies, we have uncovered ubiquitous very impulsive nonthermal emissions from the quiet sun. We refer to these emissions as Weak Impulsive Narrowband Quiet Sun Emissions (WINQSEs). Using independent observations spanning very different solar conditions we show that WINQSEs are present throughout the quiet corona at all times. Their occurrence rate lies in the range of many hundreds to about a thousand per minute, implying that on average order 10 or so WINQSEs are present in every 0.5 s MWA image. Preliminary estimates suggest that WINQSEs have a bandwidth of ~2 MHz. Buoyed by  their possible connection to the hypothesised “nanoflares”, we are pursuing several projects to characterise and understand them. These include developing machine learning algorithms to identify WINQSEs in radio images and characterise their morphologies; exploring the ability of the present generation EUV and X-ray instruments to estimate the energy corresponding to the brightest of WINQSEs; and attempting very high time resolution imaging to explore their temporal structure. In this talk, I will present the results from the past and ongoing projects about WINQSEs and argue that these might be a key step towards detecting “nanoflares” and the resolution of the coronal heating problem.</p><p> </p><p> </p>

Machine Intelligence-Based Trend Analysis of COVID-19 for Total Daily Confirmed Cases in Asia and Africa

COVID-19 is likely to pose a significant threat to healthcare, especially for disadvantaged populations due to the inadequate condition of public health services with people's lack of financial ways to obtain healthcare. The primary intention of such research was to investigate trend analysis for total daily confirmed cases with new corona virus (i.e., COVID-19) in the countries of Africa and Asia. The study utilized the daily recorded time series observed for two weeks (52 observations) in which the data is obtained from the world health organization (WHO) and world meter website. Univariate ARIMA models were employed. STATA 14.2 and Minitab 14 statistical software were used for the analysis at 5% significance level for testing hypothesis. Throughout time frame studied, because all four series are non-stationary at level, they became static after the first variation. The result revealed the appropriate time series model (ARIMA) for Ethiopia, Pakistan, India, and Nigeria were Moving Average order 2, ARIMA(1, 1, 1), ARIMA(2, 1, 1), and ARIMA (1, 1, 2), respectively.

Research on Dynamic Storage Location Assignment of Picker-to-Parts Picking Systems under Traversing Routing Method

Considering time-varying demand of online retail industry, the traditional static storage location assignment is converted into a multistage storage location assignment process based on the idea of gradual and small-step-forward optimization, which can respond to rapid changes in demand by adjusting the storage location of SKUs in the warehouse in real time and dynamically. First, the study formulates the framework dynamic storage location assignment. Then, the adjustment gain model of dynamic storage location assignment is built, and a genetic algorithm is designed to find the final adjustment solution. Finally, the computer program is developed to simulate the whole process. Simulation and data analysis results show that dynamic storage location assignment can effectively improve picking efficiency when the average order size is small and large demand correlation strength. Dynamic storage location assignment simplifies the warehouse operation process by combining the picking operation and storage location assignment into one without changing the picker’s current walking route, which can offer some theoretical guidance for online retail enterprises implementing dynamic storage location assignment.

A Study on Optimistic need of Artificial Intelligence in Fashion Retail Industry

The fashion e-commerce market has been growing steadily in the past few years accounting for USD 371 billion or 21% retail sales of apparel and footwear globally in 2019. But as most of the worlds are experiencing self-isolation and lockdown measures, the corona virus crisis is pushing brands to digitalize even faster to survive, engage with customers, designers, manufactures and redesign their supply chain operations. Many sectors are reeling from the fallout of the COVID-19 pandemic as they stare into the abyss of the impending recession and fashion has not been immune. But aside from economic factors, the industry is also facing lasting structural change. Artificial Intelligence optimizes conversion, Average Order Value (AOV) and repeat purchase rate by understanding a customer’s preferences and suggesting the right products and outfits for them. Recommendations are tailored to the physical stores with latest technologies by implementing virtual trail room, regional trends, as well as the customers’ body type, color, desired occasions and personal style.

ANALYTICAL RESULTS ON THE SERVICE PERFORMANCE OF STOCHASTIC CLEARING SYSTEMS

Stochastic clearing theory has wide-spread applications in the context of supply chain and service operations management. Historical application domains include bulk service queues, inventory control, and transportation planning (e.g., vehicle dispatching and shipment consolidation). In this paper, motivated by a fundamental application in shipment consolidation, we revisit the notion of service performance for stochastic clearing system operation. More specifically, our goal is to evaluate and compare service performance of alternative operational policies for clearing decisions, as quantified by a measure of timely service referred to as Average Order Delay ( $AOD$ ). All stochastic clearing systems are subject to service delay due to the inherent clearing practice, and $\textrm {AOD}$ can be thought of as a benchmark for evaluating timely service. Although stochastic clearing theory has a long history, the existing literature on the analysis of $\textrm {AOD}$ as a service measure has several limitations. Hence, we extend the previous analysis by proposing a more general method for a generic analytical derivation of $\textrm {AOD}$ for any renewal-type clearing policy, including but not limited to alternative shipment consolidation policies in the previous literature. Our proposed method utilizes a new martingale point of view and lends itself for a generic analytical characterization of $\textrm {AOD}$ , leading to a complete comparative analysis of alternative renewal-type clearing policies. Hence, we also close the gaps in the literature on shipment consolidation via a complete set of analytically provable results regarding $\textrm {AOD}$ which were only illustrated through numerical tests previously.