Miracle-dz/UniFAR-FTUs huggingface dataset:
SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN KARYAWAN TERBAIK MENGGUNAKAN METODE ANALYTICAL HIERARCHY PROCESS (STUDI KASUS PADA PT. BANDO INDONESIA)

Dalam penentuan karyawan terbaik pada PT. Bando Indonesia terdapat beberapa faktor yang menjadi penilaian. Penilaian ini didasarkan pada hasil cheking, jam kerja, shift kerja, data afkir dan data klaim. Sehingga tidak menutup kemungkinan bahwa pengambilan suatu keputusan merupakan suatu yang sangat penting dalam menentukan keputusan yang harus diambil oleh decision maker. Dengan tujuan untuk membangun dan memberikan alternatif sebuah sistem penunjang keputusan yang mempunyai kemampuan analisa pemilihan performa karyawan berprestasi dengan menggunakan metode Analytic Hierarchy Process (AHP), dimana masing-masing kriteria dalam hal ini faktor- faktor Kriteria dan keputusan alternatif dalam hal ini para karyawan dibandingkan satu dengan yang lainnya sehingga memberikan output nilai intensitas prioritas yang menghasilkan score nilai karyawan yang memberikan penilaian terhadap setiap kinerja karyawan berprestasi. Sistem penunjang keputusan ini membantu dan memberikan alternatif dalam melakukan penilaian setiap karyawan, melakukan perubahan kriteria, dan perubahan nilai bobot. Hal ini berguna untuk memudahkan pengambil keputusan yang terkait dengan masalah pemilihan karyawan berprestasi, sehingga akan di dapatkan karyawan yang paling layak diberi reward atau penghargaan.
Research on Fresh Quality Detection of Citrus Based on Computer Vision and Neural Network

In this study, the Tribute Citru was taken as the research object. The color image of Tribute Citru was segmented under B component of color image collected by computer vision system, and the hue frequency sequence of Tribute Citru was extracted under H component of color image as the color feature of Tribute Citru epidermis. Then the structure of the neural network was constructed and optimized, and the mapper structure for nondestructive detection of Tribute Citru fresh quality was 141-8-1. The results showed that the accuracy of computer vision and neural network technology in detecting the taste parameters of fresh Tribute Citru was 86.67%.
Arabic Handwritten Characters Recognition using Convolutional Neural Network

Recognition of Arabic handwritten characters is very important due to its various benefits and usages. Ancient documents, bank processing, postal mailing and others are examples where we may need character recognition systems. But many obstacles may be faced due to diversity of human writing styles. Language characters recognition has been widely covered in many languages and many algorithms and paradigms were used. With the strong appealing of deep CNN classifier promise results were reached in many classification problems. CNN is a feed forward neural network that is extensively used in several applications such as image classification. The main benefit of using CNN is the merging of feature extraction and classification itself. Some researchers used CNN in Arabic character recognition, one of those El-Sawy et al [1] who applied CNN architecture on a dataset namely (AHCD) of 16800 characters. They obtained a good accuracy of 94.9% and a misclassification error of 5.1% on testing data. In our paper we will explore their dataset by proposing a modified CNN architecture hopefully to overcome their results.
Machine Learning for Smart Energy Monitoring of Home Appliances Using IoT

Inefficient energy use has been a major issue globally. In Malaysia, the statistics reveal that residential energy consumption has been on a steady increase due to the growing population as well as a lack of awareness within households regarding proper energy utilization that causes significant amount of energy wastage. The emergence of Internet-of-Things (IoT) is a consequence and convergence of several key technologies such as real-time analytics, machine learning, sensors and embedded systems. The application of intelligence in IoT, known as Cognitive IoT (CIoT), will enable decision making based on historical data, and automatically train, learn and troubleshoot future issues. This project proposes a smart energy monitoring system for home appliances incorporating CIoT which consists of three parts. Firstly, a Raspberry Pi-based smart plug serving as the gateway, that is able to read current data from individual home appliances, load the trained model from training server and test the verified data using the model. Secondly, Google Colab as the training server will be used to store the training data set and building the Tensorflow-based Long Short-term Memory (LSTM) model. This recurrent neural network model will forecast electricity bill and notify users if abnormal energy consumption of individual home appliances is detected. Thirdly, a dashboard using Matplotlib library where users may monitor the real-time energy consumption. The completed LSTM model demonstrates a high accuracy of more than 80%, where a low mean squared error with train score = 0.1798 and test score = 0.1229 is determined. Furthermore, the R2 test shows a high level of goodness of fit with train score = 0.844 and test score = 0.835.
A Modified Lyapunov-Based Control Scheme for a Three-phase UPS with an Optimal Third-order Load Current Observer

This paper proposed a modified Lyapunov-based control strategy for the three-phase UPS in d-q frame, which successfully solved the inherent steady-state error problem brought by standard Lyapunov-function based method. Continually, the third-order optimal load current observer in natural frame was designed to estimate the real-time load current, resulting in the omission of three load current sensors. Finally, experimental results validated the effectiveness and efficiency of proposed approach.
A Novel Full-Soft-Switching Full-Bridge Converter With a Snubber Circuit And Couple Inductor

In this paper, a novel full-soft-switching full-bridge converter with a snubber circuit and couple inductor is proposed. The proposed converter can realize soft switching for all of the primary switches and rectifier diodes under light load condition because of the snubber circuit. And the snubber circuit can also achieve soft switching for all of its switches and diodes because of the couple inductor. In addition, the complementary symmetry control for primary-side switches is adopted so there is no shortages of duty cycle loss. The simulation and prototype results are presented to validate the proposed converter.
Fast and Seamless Handover in Software Defined Vehicular Networks

Tele-operated driving is a transition phase to the deployment of fully autonomous cars. Vehicles are supposed to communicate with a remote control station in order to exchange surrounding videos and location information. This communication is achieved through a vehicular network, and it requires low latency and high reliability. To this end, the handover management should be studied carefully. This paper devises a mobility management scheme for vehicular networks based on the Software Defined Networking (SDN) paradigm. The proposed scheme provides handover anticipation in order to guarantee a low-latency and a seamless handover. In addition, it proposes a strategy that aims at reducing control signaling overhead and network congestion. Performance analysis show that our approach reduces handover delays and losses.
An ALN Resonant Microcantilever Humidity Sensor by Activating Specific Sets of Top Electrodes Based on Graphene Oxide

The piezoelectric resonant cantilever device is one of the most promising real-time sensing platform. This paper presents an AlN microcantilever humidity sensor based on graphene oxide (GO). We separate the top electrode and sensing area to prevent top electrode and GO from interacting with each other. We have reduced the dimensions of the cantilever electrode area to improve mass sensitivity (Δf/Δm) while keep sensing area unchanged. The top electrode is divided into different groups and we improve the quality factor and signal strength of particular mode by activating specific sets of top electrodes. The sensitivity is increased to ~1kHz/10%RH when the relative humidity is greater than 80%RH. Moreover, excellent short response (<10s) and recovery time are obtained.
Extrinsic calibration of a camera-LIDAR multi sensor system using a planar chessboard

Extrinsic calibration of imaging systems containing multi-sensors have enabled unprecedented capabilities in data fusion in the fields of computer vision and robotics throughout the past few decades. In this paper, we propose a simple mutual rotation-and-translation estimation method for a multi-sensor system containing six omnidirectional RGB cameras and a common 3D Light Detection and Ranging (LIDAR) sensor using a planar chessboard pattern. We mount the sensors on a specially designed hexagonal plate, while considering each camera-LIDAR combination as an independent multi-sensor unit. For each unit, we simultaneously capture chessboard images and their three-dimensional (3D) point data at a few different orientations. Two-dimensional (2D) chess corners are reprojected into 3D space for plane fitting. RANSAC algorithm is applied on LIDAR points before they are used for plane fitting. The mutual rotation between the camera and LIDAR is calculated by aligning the normal vectors of plane fitting results. An arbitrary point from the camera plane is projected on to the LIDAR plane and the distance is minimized to estimate the mutual translation. The accuracy of this proposed method is evaluated through scene fusion experiments.
A Printed Flexible Sensor Based Silk-Fibroin for Distinguishing the Existing States of Water: Liquid or Gaseous?

Humidity is a key environmental factor, and its accurate detection is significant in many fields. However, the drawback of reaction with both gaseous and liquid water molecules extremely limits the further applications of traditional humidity sensors. Herein, we developed a novel printed flexible humidity senor based on biocompatible silk fibroin. The unique feature of water molecular selection of silk fibroin enables the proposed sensor to distinguish the existing states of water (i.e., liquid or gaseous) in the air. A printing process of functional conductive patterns on polymeric substrate is successfully developed, which endows the device with remarkable precision and flexibility.
Street-Level Landmark Evaluation With Upper Error Bound

Street-level landmarks are the basis of high-precision IP geolocation, and the location-error of landmark affects the accuracy of geolocation result. However, existing landmark evaluation methods cannot determine the error range of landmarks. Therefore, a street-level landmark evaluation algorithm that can estimate the upper bound of landmark error is proposed. Firstly the city of candidate landmarks are verified by IP location databases. Secondly, candidate landmarks are grouped through their last-hop routers, and then divided into several clusters by E-Apriori algorithm based on their location. Thirdly, the Land-mark reliability probability model is used to calculate the probability of the last-hop router location range. Finally the upper error bound of the landmark is determined by the position of the last-hop router. By verifying 503 reliable land-marks evaluated by the algorithm in Hong Kong, we find that the algorithm can determine the upper error bound of landmarks, and the accuracy reaches 100%. We test the algorithm based on 100 accurate landmarks and 400 unreliable landmarks, and find that our algorithm can evaluate 84 accurate landmarks and 1 invalid landmark, i.e., it achieves an accuracy of 98.8%. Finally, we take landmark evaluation experiment based on 50,000 candidate landmarks in Hong Kong and Zhengzhou respectively. The results show that geolocation errors decrease obviously using our reliable landmarks, and the mean error of 100 targets in Hong Kong is reduced from 4.18 km to 2.78 km.
Leveraging Change Blindness for Haptic Remapping in Virtual Environments

Passive haptic feedback provides an inexpensive and convenient approach to virtual touch. However, this approach requires that all virtual objects represented by physical props. In this paper we present change blindness haptic remapping—a novel approach that leverages change blindness to map two or more virtual objects onto a single physical prop. We describe a preliminary evaluation comparing the proposed approach to a control condition where all virtual objects where mapped to physical props. The study revealed no notable differences in terms of the participants’ experience and less than one fourth of the participants noticed the manipulation. However, the participants did perform interaction errors when exposed to haptic remapping. Based on the findings, we discuss improvements to the proposed approach and potential directions for future work.
Droplet Delivery Control for Surface Acoustic Wave Nebulization Mass Spectrometry

We present a novel atomizer using standing wave type surface acoustic wave nebulization (SAWN). Compared with progressive wave SAWN, the standing wave SAWN creates smaller drop sizes. We integrate droplet delivery control functionality with anisotropic ratchet conveyors (ARC) on top of the SAWN surface, which employs micro-sized hydrophilic patterns on hydrophobic Cytop coatings. By adopting Parylene-C as a stencil mask, the hydrophobicity of Cytop does not degrade during fabrication. Droplet movement, merging, mixing and nebulizing are demonstrated as a lab-on-chip platform.
Dynamic Spectrum Assignment Algorithm Based on Fuzzy Logic to Reduce Fragmentation in EONs

Elastic optical networks based on the optical orthogonal frequency-division multiplexing technology have been studied as promising next-generation optical backbone networks with large transport capacity and bandwidth flexibility. A main challenge in the EONs is to reduce spectrum fragmentation that decrease the network performance due to dynamic setting up and tearing down of non-uniform bandwidth requests. It is quite complex problem to solve the spectrum fragmentation. Fuzzy logic system that resembles human inference has been used for decision making for complex problem. In this paper, we propose the dynamic spectrum assignment (SA) algorithm for minimizing the fragmentation based on the fuzzy logic in EONs. To design fuzzy logic control system for the SA algorithm, we define spectrum fragmentation ratio(SFR) and time fragmentation ratio(TFR) for input membership. We also define the weight values of available spectrum blocks as output membership. The spectrum block with the minimum crisp value is selected for the spectrum assignment of connection request. The performance of the proposed algorithm is evaluated in terms of the average bandwidth blocking probability and the spectrum utilization.
Users’ consultation process in building a land cover and land use database for the official Walloon Georeferential

INSPIRE and PSI directives induced major changes in the Walloon geomatics ecosystem. INSPIRE aims at establishing an infrastructure for spatial information in the European community for environmental policies. PSI is the European legislation on the re-use of public sector information, including all government-held geodatasets. For compliance purposes, Wallonia proposed to develop a Georeferential aiming at providing free, open-access and consistent reference geodatasets to the Walloon stakeholders. Some of the current data being outdated, the Georeferential proposes pilot projects for a collaborative and user-oriented design of new maps. One of the categories being identified for the Georeferential is land cover and land use. Build on the knowledge of past researches, the Walous project aims at combining very high spatial with very high temporal resolution Earth observation and thematic data in a classification scheme. To ensure that the new data covers the general interest and to ensure his appropriation by the users, gathering requirements is one of the most vital pieces. Poor gathering of user needs could lead to an unused final product. Walous carried out interviews for clarifying data requirements. This paper introduces this methodology and illustrates how the results impacted the priorities and the specifications of the land cover and land use data.
High Energy Ion Implantation: Novel Approach on “as-implanted” Dose Control Concept

Precise vertical doping profiles in the micrometer range are key components for drift layer formation of SiC-based high voltage power devices. In order to simplify multiple high-energy implantation sequences for creating high precision deep vertical doping profiles, recently the novel “energy filter” technology, which uses just one implantation step for complete profile formation, has been developed. An energy filter is a patterned silicon membrane, which transforms a high-energy monoenergetic ion beam into a beam with a pre-defined continuous range of ion energies, resulting in custom tailored deep impurity profiles. This approach poses new demands to ion dose monitoring, which, if not addressed properly, may limit MeV ion implantation application in wafer production. This work presents first results on concepts that are suited to provide fast and reliable implanted dose analysis, immediately after ion implantation in the several MeV range. High-energy ion implantation experiments were performed with and without energy filter, using different polymers as target materials and various implant doses. Target samples were analyzed in terms of their optical and geometrical properties changes. The results of the analysis, suggestions and limiting artefacts will be discussed.
Bayesian Network Based Reliability Prediction in Mobile Wireless Networks

In 5G and future 6G cellular network systems, the reliability and prediction of network service are crucial, because they are an important part of network automation. This paper proposes a service prediction model with throughput as a reliability indicator. We generate wireless network data by simulation and use Bayesian network method to learn and predict the reliability of the network. In the feature engineering, the load of the base station, the distance between the user and the base station, and the propagation parameters between the user and the base station all affect the signal to interference plus noise ratio (SINR) of the mobile user. Simulation experiments show that the prediction results are satisfactory in the case that low load at the base station, small distance between the user and base station and a small amount of shadowing in the propagation environment.
Fast Predictive Repair in Erasure-Coded Storage

Erasure coding offers a storage-efficient redundancy mechanism for maintaining data availability guarantees in large-scale storage clusters, yet it also incurs high performance overhead in failure repair. Recent developments in accurate disk failure prediction allow soon-to-fail (STF) nodes to be repaired in advance, thereby opening new opportunities for accelerating failure repair in erasure-coded storage. To this end, we present a fast predictive repair solution called FastPR, which carefully couples two repair methods, namely migration (i.e., relocating the chunks of an STF node) and reconstruction (i.e., decoding the chunks of an STF node through erasure coding), so as to fully parallelize the repair operation across the storage cluster. FastPR solves a bipartite maximum matching problem and schedules both migration and reconstruction in a parallel fashion. We show that FastPR significantly reduces the repair time over the baseline repair approaches via mathematical analysis, large-scale simulation, and Amazon EC2 experiments.
Simulation of quantum light sources using the self-consistently coupled Schrödinger-Poisson-Drift-Diffusion-Lindblad syste

The device-scale simulation of electrically driven quantum light sources based on semiconductor quantum dots requires a combination of the (classical) semiconductor device equations with cavity quantum electrodynamics. In this paper, we extend our previously developed hybrid quantum-classical model system – where we have coupled the drift-diffusion system with a Lindblad-type quantum master equation – by including a self-consistent Schrödinger–Poisson problem. The latter describes the (quasi-)bound states of the quantum dot carriers. The extended model allows to describe the bias-dependency of the emission spectrum due to the quantum confined Stark effect.
Using Morpheme-Level Attention Mechanism for Turkish Sequence Labelling

With deep learning being used in natural language processing problems, there have been serious improvements in the solution of many problems in this area. Sequence labeling is one of these problems. In this study, we examine the effects of character, morpheme, and word representations on sequence labelling problems by proposing a model for the Turkish language by using deep neural networks. Modeling the word as a whole in agglutinative languages such as Turkish causes sparsity problem. Therefore, rather than handling the word as a whole, expressing a word through its characters or considering the morpheme and morpheme label information gives more detailed information about the word and mitigates the sparsity problem. In this study, we applied the existing deep learning models using different word or sub-word representations for Named Entity Recognition (NER) and Part-of-Speech Tagging (POS Tagging) in Turkish. The results show that using morpheme information of words improves the Turkish sequence labelling.
Surface EMG signals based motion intent recognition using multi-layer ELM

The upper-limb rehabilitation robot is regard as a useful tool to help patients with hemiplegic to do repetitive exercise. The surface electromyography (sEMG) contains motion information as the electric signals are generated and related to nerve-muscle motion. These sEMG signals, representing human’s intentions of active motions, are introduced into the rehabilitation robot system to recognize upper-limb movements. Traditionally, the feature extraction is an indispensable part of drawing significant information from original signals, which is a tedious task requiring rich and related experience. This paper employs a deep learning scheme to extract the internal features of the sEMG signals using an advanced Extreme Learning Machine based auto-encoder (ELMAE). The mathematical information contained in the multi-layer structure of the ELM-AE is used as the high-level representation of the internal features of the sEMG signals, and thus a simple ELM can post-process the extracted features, formulating the entire multi-layer ELM (ML-ELM) algorithm. The method is employed for the sEMG based neural intentions recognition afterwards. The case studies show the adopted deep learning algorithm (ELM-AE) is capable of yielding higher classification accuracy compared to the Principle Component Analysis (PCA) scheme in 5 different types of upper-limb motions. This indicates the effectiveness and the learning capability of the ML-ELM in such motion intent recognition applications.
A Wireless Parylene-Based Cardiovascular Pressure Sensor with Mxene Film

This work outlines the development of a wireless, flexible passive pressure sensor for monitoring cardiovascular pressure. The device employs Parylene C as a flexible substrate and is capable of being folded into a stent that is suitable for a catheter-based delivery system. An inductor–capacitor (LC) resonant tank, which consists of an inductor coil and a parallel-plate capacitor, was implemented for pressure sensing and wireless signal transmission. A multilayer MXene film embedded on the capacitor was used to enhance the pressure-sensing performance. External pressure on the device changes its capacitance, inducing a shift in the resonant frequency, which can be wirelessly retrieved using the phase-dip technique. The pressure responsivity of the proposed device is 0.093 MHz/mmHg, and the corresponding pressure sensitivity is 355 ppm/mmHg in the 0–250 mmHg pressure range.