Tenure, Wage Profiles and Monitoring

Efficiency wage theory predicts that firms can induce worker effort by the carrot of high wages and / or the stick of monitoring worker performance. Another option available to firms is to tilt the remuneration package over time such that the lure of high future earnings acts as a deterrent to current shirking. In this paper we undertake the first empirical investigation of this relationship between the slope of the wage-tenure profile and the level of monitoring. On the assumption that firms strive for the optimal trade-off between these various instruments, we hypothesise that increased monitoring leads to a decline in the slope of the wage-tenure profile. Our empirical analysis, using two cross sections of matched employer-employee British data, provides robust support for this prediction.Monitoring, Tenure, Efficiency, Wages.

Tenure, Wage Profiles and Monitoring

We undertake the first empirical investigation of the relationship between the slope of the wagetenure profile and the level of monitoring. On the assumption that firms strive for the optimal trade-off between these various instruments, we hypothesise that increased monitoring leads to a decline in the slope of the wagetenure profile. Our empirical analysis, using two cross sections of matched employer-employee British data, provides robust support for this prediction.efficiency wages; tenure; monitoring

Data-mining modeling for the prediction of wear on forming-taps in the threading of steel components

An experimental approach is presented for the measurement of wear that is common in the threading of cold-forged steel. In this work, the first objective is to measure wear on various types of roll taps manufactured to tapping holes in microalloyed HR45 steel. Different geometries and levels of wear are tested and measured. Taking their geometry as the critical factor, the types of forming tap with the least wear and the best performance are identified. Abrasive wear was observed on the forming lobes. A higher number of lobes in the chamber zone and around the nominal diameter meant a more uniform load distribution and a more gradual forming process. A second objective is to identify the most accurate data-mining technique for the prediction of form-tap wear. Different data-mining techniques are tested to select the most accurate one: from standard versions such as Multilayer Perceptrons, Support Vector Machines and Regression Trees to the most recent ones such as Rotation Forest ensembles and Iterated Bagging ensembles. The best results were obtained with ensembles of Rotation Forest with unpruned Regression Trees as base regressors that reduced the RMS error of the best-tested baseline technique for the lower length output by 33%, and Additive Regression with unpruned M5P as base regressors that reduced the RMS errors of the linear fit for the upper and total lengths by 25% and 39%, respectively. However, the lower length was statistically more difficult to model in Additive Regression than in Rotation Forest. Rotation Forest with unpruned Regression Trees as base regressors therefore appeared to be the most suitable regressor for the modeling of this industrial problem.ThisinvestigationwaspartiallysupportedbyProjects TIN2011-24046,IPT-2011-1265-020000andDPI2009- 06124-E/DPIoftheSpanishMinistryofEconomyand Competitiveness.WethanktheUFIinMechanicalEngineer- ing oftheUPV/EHU(UFIMECA-1.0.2016(ext))forits support

A Data-driven Fault Isolation and Identification Scheme for Multiple In-Phase Faults in Satellite Control Moment Gyros

A satellite can only complete its mission successfully when all its subsystems, including the attitude control subsystem, are in healthy condition and work properly. Control moment gyroscope is a type of actuator used in the attitude control subsystems of satellites. Any fault in the control moment gyroscope can cause the satellite mission failure if it is not detected, isolated and resolved in-time. Fault isolation provides an opportunity to detect and isolate the occurring faults and, if accompanied by proactive remedial actions, can avoid failure and improve the satellite reliability. It is also necessary to know the fault severity for better maintenance planning and prioritize the corrective actions. This way, the more severe faults can be corrected first. In this work, an enhanced data-driven fault diagnosis scheme is introduced for fault isolation and identification of multiple in-phase faults of satellite control moment gyroscopes that is not addressed in the literature before with high accuracy. The proposed method is based on an optimized support vector machine and an optimized support vector regressor. The results yield fault predictions with up to 95.6% accuracy for isolation and 94.9% accuracy for identification, on average. In addition, a sensitivity analysis with regards to noise, missing values, and missing sensors is done where the results show that the proposed model is robust enough to be used in real applications

Ambient Electromagnetic Radiation as a Predictor of Honey Bee (\u3ci\u3eApis mellifera\u3c/i\u3e) Traffic in Linear and Non-Linear Regression: Numerical Stability, Physical Time and Energy Efficiency

Since bee traffic is a contributing factor to hive health and electromagnetic radiation has a growing presence in the urban milieu, we investigate ambient electromagnetic radiation as a predictor of bee traffic in the hive’s vicinity in an urban environment. To that end, we built two multi-sensor stations and deployed them for four and a half months at a private apiary in Logan, Utah, U.S.A. to record ambient weather and electromagnetic radiation. We placed two non-invasive video loggers on two hives at the apiary to extract omnidirectional bee motion counts from videos. The time-aligned datasets were used to evaluate 200 linear and 3,703,200 non-linear (random forest and support vector machine) regressors to predict bee motion counts from time, weather, and electromagnetic radiation. In all regressors, electromagnetic radiation was as good a predictor of traffic as weather. Both weather and electromagnetic radiation were better predictors than time. On the 13,412 time-aligned weather, electromagnetic radiation, and bee traffic records, random forest regressors had higher maximum R2 scores and resulted in more energy efficient parameterized grid searches. Both types of regressors were numerically stable

Data-driven solutions to enhance planning, operation and design tools in Industry 4.0 context

This thesis proposes three different data-driven solutions to be combined to state-of-the-art solvers and tools in order to primarily enhance their computational performances. The problem of efficiently designing the open sea floating platforms on which wind turbines can be mount on will be tackled, as well as the tuning of a data-driven engine's monitoring tool for maritime transportation. Finally, the activities of SAT and ASP solvers will be thoroughly studied and a deep learning architecture will be proposed to enhance the heuristics-based solving approach adopted by such software. The covered domains are different and the same is true for their respective targets. Nonetheless, the proposed Artificial Intelligence and Machine Learning algorithms are shared as well as the overall picture: promote Industrial AI and meet the constraints imposed by Industry 4.0 vision. The lesser presence of human-in-the-loop, a data-driven approach to discover causalities otherwise ignored, a special attention to the environmental impact of industries' emissions, a real and efficient exploitation of the Big Data available today are just a subset of the latter. Hence, from a broader perspective, the experiments carried out within this thesis are driven towards the aforementioned targets and the resulting outcomes are satisfactory enough to potentially convince the research community and industrialists that they are not just "visions" but they can be actually put into practice. However, it is still an introduction to the topic and the developed models are at what can be defined a "pilot" stage. Nonetheless, the results are promising and they pave the way towards further improvements and the consolidation of the dictates of Industry 4.0

Minimization of defects generation in laser welding process of steel alloy for automotive application

Laser welding (LW) thanks to its flexibility, limited energy consumption and simple realization has a prominent role in several industrial sectors. LW process requires careful parameters' tuning to avoid generating internal defects in the microstructure or a poor weld depth, which reduce the joining mechanical strength and result in waste. This work exploits a supervised machine learning algorithm to optimize the process parameters to minimize the generated defects, while catering for design specifications and tolerances to predict defect generation probability. The work outputs a predictive quality control model to reduce non-destructive controls in the LW of aluminum for automotive applications

Earthquake Early Warning and Preparatory Phase Detection through the use of Machine Learning Techniques

In this thesis I present 3 different works developed during the PhD. These three works are already published. My research has been focused on onsite EEW techniques oriented to the seismic risk reduction for buildings. As matter of fact, in the first work (Iaccarino et al., 2020; Chapter 2), "Onsite earthquake early warning: Predictive models for acceleration response spectra considering site effects" , we presented an EEW method that predict Response Spectra of Acceleration (RSA) at nine different periods from P-wave parameters (i. e., Pd and IV2) on 3s window. RSA is a ground motion parameter of particular interest for structural engineers since it better correlates with structural damage than peak parameters such as PGA and PGV (Elenas and Meskouris, 2001). To account for site-effects, we retrieved a partially non-ergodic model using a mixed-effect regression analysis. This procedure helped us to reduce the prediction uncertainty. Finally, we analyzed the correction terms by station, and we found that the stations with the more positive ones (grater RSA) were the same stations to have amplification effects highlighted by H/V analysis. Furthermore, our models improve the EEW performances both in terms of true negatives and false positives. The second work I present, "Earthquake Early Warning System for Structural Drift Prediction using Machine Learning and Linear Regressors" (Iaccarino et al., 2021; Chapter 3), uses data recorded from in-building sensors from Japanese and Californian structures. Here, we developed a method to predict Structural Drift using P-wave features (i. e., Pd, IV2, and ID2) from 1s, 2s, and 3s windows. We studied the effects of the complexity of the dataset on the predictions subdividing the Japanese dataset in three subsets: data from one building; data from buildings with the same material of construction; entire dataset. From this study, we found that the variability of the dataset plays a key role in the predictions increasing the uncertainties of the predictions for the complete dataset. Moreover, we compared the performances of linear least square models and non-linear machine learning regressors finding that the last ones perform always better. In the end, we tried to export the model retrieved on Japanese buildings to the Californian buildings, finding that the drift predictions are underestimated by a bias. We proposed to correct this bias using magnitude dependent correction terms, finding that the linear models are more able to adapt in these conditions. In the end, I present "Forecasting the Preparatory Phase of Induced Earthquakes by Recurrent Neural Network" (Chapter 4; Picozzi and Iaccarino, 2021). Here, we used catalogue information from a very complete dataset of the Californian geothermal area, The Geysers. From the catalogue, we chose 8 events with M>=3.9, and we selected the first 5 as training set and rest as testing set. Then, we extracted 9 features as time-series: the b-value and completeness magnitude, Mc, of the Gutenberg-Richter law; the fractal dimension of hypocenters, Dc; the generalized distance between pairs of earthquakes, η; the Shannon's information entropy, h; the moment magnitude, Mw, and moment rate, M ̇_0; the total duration of event groups, ΔT, and the inter-event time, Δt. We wanted to assess the possibility to detect changes in time of these features that can be related to deviations from the background seismicity. We built two Recurrent Neural Networks, one to detect preparatory phase the other to detect the aftershocks phase. The method is able to discriminate both the preparatory phase and the aftershock phase on the testing set. In the end, merging the predictions of two methods, we found that all the three events in testing set present a preparatory phase that lasts from 4 hours to 2 days before the main event

Flooding prevention in distillation and extraction columns with aid of machine learning approaches

Flooding of separation columns is a severe limitation in the operation of distillation and liquid-liquid extraction columns. To observe operation conditions, machine learning algorithms are implemented to recognize the flooding behavior of separation columns on laboratory scale. Besides this, the investigated columns already provided the modular automation interface Module Type Package (MTP), which is used for data access of necessary sensor data. Hence, artificial intelligence (AI) tools with deep learning offer high potential for the process industry and allow to capture operating states that are otherwise difficult to detect or model. However, the advanced methods are only hesitantly applied in practice due to complex combination of operational sensing, data analysis, and active control of the equipment. This article provides an overview on how AI-based algorithms can be implemented in existing laboratory plants. Process sensor data as well as image data are used to model the flooding behavior of distillation and extraction columns for stable and robust operational conditions