Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of a Configure-to-Order-Based Process for the Implementation of Modular Product Architectures: A Case Study

As todays’ global market trends lead to an increasing demand for individualised products, manufacturers need to cope with a high degree of internal and external variety, which has a severe impact on complexity and therefore -costs. When implementing modular product architectures, it becomes obvious, that the actual Engineer-to-Order (ETO) processes cannot cope with the requirements of such a product architecture. It is crucial to develop a complying Configure-to-Order (CTO) process in order to make full use of its suppled benefits. As there is no existing approach about how to methodically change an existing ETO process into an adequate CTO process, we intend to fill this gap with this paper by showing an approach for the development of a CTO process for modular product architectures. Furthermore, we show the application and evaluation of this approach in a case study with a special equipment manufacturer (SME), that is already implementing modular architectures.
Treinamento de redes neurais com arquitetura multilayer perceptron em FPGA

This course conclusion paper presents an implementation on an FPGA of a system responsible for the on chip training of neural networks with MultiLayer Perceptron architecture. The adopted method for training is the Mini Batch Stochastic Gradient Descent acelerating convergence with momentum. The study of the networks and the training methods was carried out initially in Python with the aid of the machine learning library Scikit-learn. Subsequently in MatLab the routines responsible for the training were generated with fixed-point arithmetic in order to facilitate the translation to a Hardware Description Language (HDL). In the next stage, the training on the HDL Verilog was implemented considering a fixed topology, activation function Rectified Linear Unit (ReLU) and cost function as the Mean Squared Error (MSE). The results obtained with Python are compared with those of MatLab checking for congruence as the results can’t be equals due to inerent loss from quantization using fixed-point calculations. Afterwards the system implemented in Verilog is simulated in the ModelSim software and the results compared to those of MatLab whose values must be similar, thus validating the work done.
Problem specific heuristics for group scheduling problems in cellular manufacturing

Heuristics based on dispatching rules are still widely used in practice as methods for effective scheduling systems. Despite the successful development of various novel dispatching rules for traditional job shop scheduling environments, nearly none of these has been applied to cellular manufacturing within the last decade. In this paper, we close this gap by implementing novel dispatching rules into two-stage heuristics in order to solve group scheduling problems in a job shop manufacturing cell. By a comprehensive simulation study these heuristics are evaluated and compared to established effective dispatching rules. It is shown that some new heuristics are capable of leading to superior results compared to previous heuristics with respect to mean flow time and mean tardiness. Besides, several influencing factors for the heuristics’ performance are analyzed and statistically evaluated.
Real time risk monitoring in a processing system using Bayesian networks

Safety and risk are essential components of process industries. The research objective of this thesis is to develop a method to measure and monitor safety in terms of real-time risk of a process system failure. The risk monitoring concept was developed using event trees and Bayesian networks. Process instrument data such as flowrate was used as a basis for the risk probability calculations. The risk monitoring methodology was developed and applied to the Williams Geismar reboiler rupture and fire in 2013. The risk level of the reboiler was examined based on the original design prior to failure and an updated design based on recommendations made by the CSB. The accident probability was decreased by 96% and risk level was reduced by 76.9%. By plotting the risk of a process overtime, future projections of risk can be predicted and action can be taken to prevent accidents before they could occur.
A defogging method based on weigh least square filtering

Image captured in foggy weather conditions often suffer from bad visibility,it is critical to restore and preserve edge detail in image defogging.In this paper,we propose a fog-free method from a single image based on weigh least square filtering which can preserve edge details well and smooth texture,First, the atmospheric veil is estimated by making use of the weigh least square filtering, so that we can obtain the atmospheric transmittance distribution. Then, estimating the atmospheric light by dark channel prior theory.Finally, the fog-free image is recovered by transforming the atmospheric scattering model. The experimental results show that the algorithm has a small time complexity, and effectively restore the fog image, which is beneficial to the realization of real-time defogging.
Analysis of the Effect of the Reliability of the NB-Iot Network on the Intelligent System

With the rapid development of the Internet of Things (IoT) in recent years, the popularization of different segments of IoT has emerged. The IoT technology is slowly evolving toward intelligence, convenience, low power consumption, large connectivity, and wide coverage. This evolution is significantly attributed to the emergence of Narrowband of Internet of Thing (NB-IoT). NB-IoT is an emerging technology with wide-coverage, large-connection, low-power consumption, and low-costs. The intelligent system based on NB-IoT, an important branch of the IoT field, has various functions and is widely used. However, the intelligent system based on NB-IoT also generates reliability problems, caused by hardware, software, and networks. Therefore, this study deeply evaluates the impact of the NB-IoT network on the reliability of IoT intelligent systems. We simulated the real-world scenario by using the ns-3 simulator, and then carried out several data transmission experiments and recorded the relevant data of transmission performance indicators, finally calculated and analyzed the reliability results by using quantitative reliability indicators. The three main experiments respectively change the transmission distance of the NB-IoT signal, increase the access amount of the NB-IoT signal in the system, and increase the obstacles in the transmission path. The experimental results are represented by the signal-to-interference ratio (SINR), throughput rate, packet loss rate, and correct rate of the received data block and loss. Finally, we verify the impact of the NB-IoT network on system reliability on the basis of the experimental results.
Nonlinear least square approach for range estimation based on attenuation of EM waves in seawater using world ocean data from 1955 to 2012

In this research paper, we estimated absorption loss (Lα) and spreading loss (Lo) based on electromagnetic (EM) waves propagation, considering multiple seawater depth (1101) points from surface to 5500 m distributed uniformly with 5‐m difference. Estimation of parameters aforementioned was done based on world ocean data (WOD13) from the National Center for Environmental Information (NCEI) averaged between 1955 and 2012 along decades for basic parameters of seawater T (Co) and S (ppt) up to 5500‐m depth vertically and also across (41 088) latitude/longitude points for all oceans that includes Indian, Pacific, Southern, Atlantic, and Arctic horizontally. We also computed another important factor that contributes in overall path loss (LUW), loss due to polarization of EM fields between transmitter Tx and receiver Rx such as antenna polarization factor (Lϕ). Lϕ (dB) along with Lα (dB) and Lo (dB) by substituting into basic propagation model for EM waves in seawater helps us to predict efficiently accurate achievable range (Rest) using nonlinear least square (NLLS) approximation combined with Lambert transformation considering nonlinear exponential PT (dBm) decay. Moreover, predicted Rest (m) helps us to minimize mean error (mean(e(t))) by adapting to actual range R (m) using NLLS approach. Simulation tool MATLAB has been used for implementation of NLLS approach and performance analysis.
Improving the processing of machine vision images of robotic systems in the Arctic

The transformation of the development of the Arctic is due to modern robotic systems. The use of unmanned vehicles in many industries in the Arctic provides an array of photo and video information. Accuracy of image analysis and pattern recognition is enhanced by image preprocessing. However, the existing binarization algorithms are not universal for images with different distortions and loss of information. The accuracy of binarization algorithms depends on many factors, such as shadows, uneven lighting, low contrast, noise, etc. Images with different characteristics of light and noise are simulated in order to model various lighting conditions on information from digital cameras of robotic systems. The paper investigates global and adaptive image binarization algorithms. The binarized images were obtained using these algorithms and the results of binarized images recognition are compared by an optical character recognition system. An analysis of the comparison results showed that for images made in poor lighting conditions or images with low contrast, or images with high noise levels, adaptive binarization algorithms are better suited. However, in most cases it is not possible to obtain fully correctly recognized images.The paper proposes a new binarization method based on artificial neural networks. The process of creating an artificial neural network is shown, include the parameters for determining the class of a pixel, the adjustment of weights, the architecture of an artificial neural network. A comparison of the proposed artificial neural network with existing image binarization algorithms demonstrate that in most cases the artificial neural network has the result of image processing at the level of adaptive algorithms or higher. The proposed method of images binarization based on the image color characteristics analysis allows to solve image recognition tasks by robotized systems.
Physical Layer Security for the Smart Grid: Vulnerabilities, Threats, and Countermeasures

Smart energy systems are becoming an important component of smart cities. The wide adoption of existing computing technologies and communication standards by a smart energy system exposes it to the plethora of threats that exist in cyberspace. In this article, we investigate the vulnerabilities and threats associated with smart energy system components, including Internet of Things enabled devices, as well as relevant communication standards, and we discuss countermeasures against adversarial attacks. We found that the existing literature has reported various attacks, including modification, denial-of-service, malware, and message replay, which can cause malfunctioning of different components of a smart energy system. In addition, we propose a framework for securing the physical layer of the smart energy system. The framework is based on advanced key generation, machine learning, and physical layer security techniques, which enhance the security of smart energy systems across different applications.
Distance Distribution in Reed-Solomon Codes

Let <inline-formula> <tex-math notation="LaTeX">$\mathbb {F}_{q}$ </tex-math></inline-formula> be the finite field of <inline-formula> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> elements. In this paper we obtain bounds on the following counting problem: given a polynomial <inline-formula> <tex-math notation="LaTeX">$f(x)\in \mathbb {F}_{q} [x]$ </tex-math></inline-formula> of degree <inline-formula> <tex-math notation="LaTeX">$k+m$ </tex-math></inline-formula> and a non-negative integer <inline-formula> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula>, count the number of polynomials <inline-formula> <tex-math notation="LaTeX">$g(x)\in \mathbb {F}_{q} [x]$ </tex-math></inline-formula> of degree at most <inline-formula> <tex-math notation="LaTeX">$k-1$ </tex-math></inline-formula> such that <inline-formula> <tex-math notation="LaTeX">$f(x)+g(x)$ </tex-math></inline-formula> has exactly <inline-formula> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula> roots in <inline-formula> <tex-math notation="LaTeX">$\mathbb {F}_{q}$ </tex-math></inline-formula>. Previously, explicit formulas were known only for the cases <inline-formula> <tex-math notation="LaTeX">$m=0, 1, 2$ </tex-math></inline-formula>. As an application, we obtain an asymptotic formula on the list size of the standard Reed-Solomon code <inline-formula> <tex-math notation="LaTeX">$[q, k, q-k+1]_{q}$ </tex-math></inline-formula>.
Training quantized one-stage object detection neural networks via selective feature imitation

Abstract. The quantization of neural networks using low-precision numeric is an efficient approach to reduce the requirements for storage and computation resources, which makes it a reality to run the neural network on the resource-constrained devices. However, the accuracy of the neural network deteriorates after the normal quantization, especially for the object detection task. To overcome this dilemma, we propose a teacher–student based quantization training scheme for the one-stage object detection neural network. First, the quantization configurations of weights and activations are determined according to their statistical probability densities of each layer, thus ensuring a lower representation error of the fixed-point numeric. Second, the supplementary supervision information from a high-performance floating-point teacher detection neural network is used to assist the training of the quantized student detection neural network. Therefore, the expression and fitting ability of the quantized student can be significantly improved by selectively imitating the teacher’s key features that are indicated in the feature selection matrix. The superiority of our method has been verified on several benchmark datasets in comparison with other related methods. On the PASCAL VOC2007 test set, the mean average precision (mAP) of the 4-bit quantized tiny-YOLOv2 trained with our method can reach 61.1%, compared with an mAP of 57.4% of its 32-bit floating-point counterpart trained with the plain method. Further, the 3-bit quantized detection neural networks can still achieve satisfactory mAP even without the pretrained weights, demonstrating the robustness of the proposed method.
CPG-fuzzy-based control of a cownose-ray-like fish robot

Purpose This paper aims to imitate a cownose ray to develop a fish robot with paired flexible multi-fin-ray oscillating pectoral fins (OPFs) and control it to accomplish vivid stable 3-D motions using central pattern generators (CPGs) and fuzzy algorithm. Design/methodology/approach The cownose ray’s asymmetric sine-like oscillations were analyzed. Then a cownose-ray-like fish robot named Robo-ray was developed, which has paired flexible multi-fin-ray OPFs to actively control the fin shape and two tail fins to control the depth. To solve the problem of coordinated control for multi-degree-of-freedom Robo-ray, CPGs were adopted. An improved phase oscillator as a CPG unit with controlled amplitude, phase lag, smooth frequency transition and asymmetric oscillation characteristic was established. Furthermore, the CPG-fuzzy algorithm was developed for vivid stable 3-D motions. The open-loop speed control, the closed-loop control of depth and yaw were established. Findings The kinematic comparisons indicate that Robo-ray imitates the cownose ray realistically. The experimental results of closed-loop are obtained that the depth error of Robo-ray is less than ±100 mm and the course error is less than ±3°. Furthermore, the comprehensive experiments demonstrate that Robo-ray has high mobility, stability and robustness. Originality/value This research makes the fish robot with OPF propulsion closer to practical applications in complex underwater environment, for instance, ocean explorations, water quality monitoring and stealth military reconnaissance. In addition, Robo-ray can be taken as a scientific tool for better understanding of the hydrodynamics of OPF batoid.
Directed-Graph-Observer-Based Model-Free Cooperative Sliding Mode Control for Distributed Energy Storage Systems in DC Microgrid

With the aim to solve the problems related to the power distribution and current chattering in a distributed energy storage system (DESS), which can be considered as a multiagent system in dc microgrid, a model-free cooperative sliding mode control scheme with a directed-graph-based observer is proposed in this article. First, a state-of-charge (SoC)-based droop control strategy is combined with a directed-graph-based voltage regulation method to balance the SoCs of each energy storage unit (ESU), allocate the power, and maintain the bus voltage stability. Besides, a directed-graph-based observer is designed to estimate the battery current considering the communication between the ESUs, and based on this observer, an estimation algorithm for adaptive coefficients is developed. Then, the multiagent sliding mode control strategy with an antiwindup compensator of input constraint is developed to track the reference current with little chattering in the DESS, and the stability proof is given. Finally, simulations are conducted to validate the effectiveness and superiority of the newly designed control strategy for the DESS.
Forecasting Building Energy Consumption with Deep Learning: A Sequence to Sequence Approach

Energy Consumption has been continuously increasing due to the rapid expansion of high-density cities, and growth in the industrial and commercial sectors. To reduce the negative impact on the environment and improve sustainability, it is crucial to efficiently manage energy consumption. Internet of Things (IoT) devices, including widely used smart meters, have created possibilities for energy monitoring as well as for sensor based energy forecasting. Machine learning algorithms commonly used for energy forecasting such as feedforward neural networks are not well-suited for interpreting the time dimensionality of a signal. Consequently, this paper uses Recurrent Neural Networks (RNN) to capture time dependencies and proposes a novel energy load forecasting methodology based on sample generation and Sequence-to-Sequence (S2S) deep learning algorithm. The S2S architecture that is commonly used for language translation was adapted for energy load forecasting. Experiments focus on Gated Recurrent Unit (GRU) based S2S models and Long Short-Term Memory (LSTM) based S2S models. All models were trained and tested on one building-level electrical consumption dataset, with five-minute incremental data. Results showed that, on average, the GRU S2S models outperformed LSTM S2S, RNN S2S, and Deep Neural Network models, for short, medium, and long-term forecasting lengths.
Multi-Agent Leader-Follower Tracking Over Random Structurally Balanced Networks

In the research of multi-agent systems (MASs), structurally balanced networks (SBNs) have been widely used to explain the cooperation and competition between agents. At present, most of the research focuses on the model over SBNs with deterministic information channels. The highlight of this paper is to examine leader-follower tracking of general linear MASs over random SBNs, in which the existence of information channel between agents at each time instant is probabilistic and independent of other channels. By describing the random interaction process by the products of sub-stochastic matrices, a sufficient condition is established even with a strictly unstable system matrix, and the upper bound of eigenvalue modules closely related to the stability of the system matrix is further given. The observed leader-follower tracking performance over SBNs is verified by simulation experiments.
Heuristic Evaluation of Ambient Devices Using Smart Materials

Teaching “calm technology” and “smart materials” as prospective trends in product design is the motivation of the educational workshop presented in this paper. Materials can trigger creative thinking. Indeed, concepts can be generated ideas that come from the encounter with a material showing the material's unexpressed potential. However, a smart material is a complex hybrid object. It is a highly technical matter that requires years of R&D to be developed and matured. It is also a highly social matter, that blurs the traditional boundary between matter and function in a product, creates an experience, and enhances sensations. The workshop presented in this paper is an opportunity for the students to analyze the complexity of user experience related to ambient devices using smart materials. In order to provide a guideline to perform this analysis, an approach based on heuristic evaluation is proposed to the students.
2018_N145 – Implementação de um sistema de recomendação aplicado a um sistema de gestão de campanhas

The plethora of information available on the Internet nowadays, makes difficult for clients to find useful products for their necessities. This problem should be tackled by the industry with two main goals: first, to increase companies profits and secondly to improve quality-of-service. Hence, recommender systems are useful tools to solve this problem by providing customized and personalized recommendations for products and services. The goal of this internship is to study and develop a recommender system to incorporate in the Active Campaign Manager (ACM) platform. This platform enables telecommunications operators to manage their advertising campaigns. This study looks at several techniques that allow ACM to obtain a list of campaign recommendations that can be sent to a particular client. Recommendations should be made taking into account the characteristics of each client and their history of subscriptions in other campaigns. Taking into account the study performed on recommendation algorithms, it was considered three diferent methods for obtaining the recommendations. First, using open-source frameworks for recommender systems, the second one through the use of entreprise software that allows the implementation of recommender engines on the cloud and the final method uses the subscription history of the clients alongside their characteristics and applies clustering methods to generate the campaign recommendations. It is considered that a study that covers these three approaches will be a very complete study that will address the problem of this internship and will allow to determine the best approach for ACM.