A New Design Procedure for Rotor Laminations of Synchronous Reluctance Machines with Fluid Shaped Barriers

A new procedure for the design and optimization of the rotor laminations of a synchronous reluctance machine is presented in this paper. The configuration of the laminations is symmetrical and contains fluid-shaped barriers. The parametrization principle is used, which executes variations in the lamination geometry by changing the position, thickness and shape of the flux barriers. Hence, the optimization procedure analyzes the various configurations through finite element simulations, by means of the communication between MATLAB and Flux 2D. In the post processing stage, the best geometry which optimizes mean torque, torque ripple, efficiency and power factor is selected. Once the best rotor configuration is defined, further investigations allow improving its performance by modifying the current angle, the stator winding and the thickness of the radial ribs.

Identifikasi Website Phishing Menggunakan Algoritma Classification And Regression Trees (CART)

With the increase in internet users and the development of technology, the threats to its security are increasingly diverse. One of them is phishing which is the most important issue in cyberspace. Phishing is a threatening and trapping activity someone by luring the target to indirectly provide information to the trapper. The number of phishing crimes, this has the potential to cause several losses, one of which is namely about the loss of privacy of a person or company. This study aims to identify phishing websites. The Classification And Regression Trees (CART) algorithm is one of the classification algorithms, and the dataset in this research taken from the UCI Repository Learning obtained from the University of Huddersfield. The method used in this research is problem identification, data collection, pre-processing stage, use of the CART algorithm, validation and evaluation and withdrawal conclusion. Based on the test results obtained the value of accuracy of 95.28%. Thus the value of the accuracy obtained using the CART algorithm of 95.28% categorized very good classification.

Can a Byte Improve Our Bite? An Analysis of Digital Twins in the Food Industry

The food industry faces many challenges, including the need to feed a growing population, food loss and waste, and inefficient production systems. To cope with those challenges, digital twins that create a digital representation of physical entities by integrating real-time and real-world data seem to be a promising approach. This paper aims to provide an overview of digital twin applications in the food industry and analyze their challenges and potentials. Therefore, a literature review is executed to examine digital twin applications in the food supply chain. The applications found are classified according to a taxonomy and key elements to implement digital twins are identified. Further, the challenges and potentials of digital twin applications in the food industry are discussed. The survey revealed that the application of digital twins mainly targets the production (agriculture) or the food processing stage. Nearly all applications are used for monitoring and many for prediction. However, only a small amount focuses on the integration in systems for autonomous control or providing recommendations to humans. The main challenges of implementing digital twins are combining multidisciplinary knowledge and providing enough data. Nevertheless, digital twins provide huge potentials, e.g., in determining food quality, traceability, or designing personalized foods.

Deteksi Serangan Spoofing Wajah Menggunakan Convolutional Neural Network

Facial recognition-based biometric authentication is increasingly frequently employed. However, a facial recognition system should not only recognize an individual's face, but it should also be capable of detecting spoofing attempts using printed faces or digital photographs. There are now various methods for detecting spoofing, including blinking, lip movement, and head tilt detection. However, this approach has limitations when dealing with dynamic video spoofing assaults. On the other hand, these types of motion detection systems can diminish user comfort. As a result, this article presents a method for identifying facial spoofing attacks through Convolutional Neural Networks. The anti-spoofing technique is intended to be used in conjunction with deep learning models without using extra tools or equipment. Our CNN classification dataset can be derived from the NUAA photo imposter and CASIA v2. The collection contains numerous examples of facial spoofing, including those created with posters, masks, and smartphones. In the pre-processing stage, image augmentation is carried out with brightness adjustments and other filters so that the model to adapt to various environmental conditions. We evaluate the number of epochs, optimizer types, and the learning rate during the testing process. The test results show that the proposed model achieves an accuracy value of 91.23% and an F1 score of 92.01%.

Analisis Pengelolaan Sampah Anorganik Di Desa Parit 1 Api-Api Kecamatan Bandar Laksamana Tahun 2020

Di negara berkembang umumnya sampah masih dibuang tanpa adanya pemilahan terlebih dahulu, seperti sampah Organik, Anorganik, dan logam masih menjadi satu, itulah yang menyulitkan untuk dilakukannya penanganan. Dampak negatif yang dapat ditimbulkan oleh volume sampah yang tinggi dan tidak dikelola dengan baik dapat berupa gangguan kesehatan, menurunkan kualitas lingkungan, menurunkan estetika lingkungan. Tujuan penelitian ini adalah untuk menganalisis pengelolaan sampah anorganik di Desa Parit 1 Api- Api Tahun 2020. Penelitian ini merupakan peneltian kualitatif deskriptif yaitu dengan melakukan observasi dan wawancara mendalam. Sampel yang menjadi informan dalam penelitian ini berjumlah 8 orang, variabel yang diteliti antara lain sumber daya manusia, anggaran, sarana prasarana, metode, tahap pemilahan, tahap pengumpulan, tahap pengangkutan dan tahap pengolahan. Hasil penelitian menunjukkan tidak adanya ketersedian tenaga kerja kebersihan di desa, tidak ada pengalokasian dana khusus untuk pengelolaan sampah anorganik, kurangnya sarana dan prasarana serta tidak adanya masyarakat melakukan tahap pemilahan, pengumpulan, pengangkutan dan pengolahan dalam melakukan pengelolaan sampah anorganik di Desa Parit 1 Api-Api. Disarankan kepada Aparatur Pemerintahan Desa untuk mendirikan Bank Sampah di Desa Parit 1 Api-Api agar masyarakat lebih giat dalam pengelolaan sampah menjadi barang berguna sehingga sampah yang masih bernilai ekonomis masih bisa dipergunakan untuk peningkatan ekonomi masyarakat sekitar. In developing countries, generally waste is still discarded without prior sorting, such as organic, inorganic, and metal waste is still one, which makes it difficult for handling. The negative impacts that can be caused by high volumes of waste that are not managed properly can be in the form of health problems, reducing environmental quality, reducing environmental aesthetics. The purpose of this study was to analyze the management of inorganic waste in the Village of Parit 1Api-Apiin 2020. This researchwas a descriptive qualitative research by conducting in-depth observations and interviews. Thenumber of informants in this study amounted to 8 people, the variables studied included human resources, budget, infrastructure, methods, sorting stage, collection stage, transportation stage and processing stage. The results showed that there was no availability of sanitation workers in the village, no allocation of special funds for the management of inorganic waste, lack of facilities and infrastructure as well as the absence of the community carrying out the stages of sorting, collecting, transporting and processing in managing inorganic waste in the Village of Parit 1 Api-Api.Researchers suggest to the Village Government Apparatus to establish a Trash Bank in the Village of Trench 1 Api-Api so that the community is more active in managing waste into useful goods so that waste that is still of economic value can still be used to improve the economy of the surrounding community.

Text Mining Techniques for Identify Islamophobic Conversation Language by Selecting Preprocessing Feature

By identifying a text's polarity, sentiment analysis is a technique for extracting information from a person's attitude about an issue or occurrence. The grouping is made to discuss whether the reader is positive or negative. The drop duplication procedure creates 4339 from the preceding 10997, and the result language detection is 31 languages, thanks to the pre-processing stage. Although the data comes from the world's largest Muslim country, the problem is not limited to it, as evidenced by the use of text mining tools to identify languages.

Modulation of cutaneous responses in the cuneate nucleus of macaques during active movement

To achieve stable and precise movement execution, the sensorimotor system integrates exafferent sensory signals originating from interactions with the external world and reafferent signals caused by our own movements. This barrage of sensory information is regulated such that behaviorally relevant signals are boosted at the expense of irrelevant ones. For example, sensitivity to touch is reduced during movement - when cutaneous signals caused by skin stretch are expected and uninteresting - a phenomenon reflected in a decreased cutaneous responsiveness in thalamus and cortex. Some evidence suggests that movement gating of touch may originate from the cuneate nucleus (CN), the first recipient of signals from tactile nerve fibers along the dorsal columns medial lemniscal pathway. To test this possibility, we intermittently delivered mechanical pulses to the receptive fields (RFs) of identified cutaneous CN neurons as monkeys performed a reach-to-grasp task. As predicted, we found that the cutaneous responses of individual CN neurons were reduced during movement. However, this movement gating of cutaneous signals was observed for CN neurons with RFs on the arm but not those with RFs on the hand. We conclude that sensory gating occurs in the first processing stage along the somatosensory neuraxis and sculpts incoming signals according to their task relevance.

Glottal stops do not constrain lexical access as do oral stops

This study explores processing characteristics of a glottal stop in Maltese which occurs both as a phoneme and as an epenthetic stop for vowel-initial words. Experiment 1 shows that its hyperarticulation is not necessarily mapped onto an underlying form, although listeners may interpret it as underlying at a later processing stage. Experiment 2 shows that listeners’ experience with a particular speaker’s use of a glottal stop exclusively as a phoneme does not modulate competition patterns accordingly. Not only are vowel-initial words activated by [ʔ]-initial forms, but /ʔ/-initial words are also activated by vowel-initial forms, suggesting that lexical access is not constrained by an initial acoustic mismatch that involves a glottal stop. Experiment 3 reveals that the observed pattern is not generalizable to an oral stop /t/. We propose that glottal stops have a special status in lexical processing: it is prosodic in nature to be licensed by the prosodic structure.

Learning a new class of multisensory associations: High-density electrophysiological mapping of the temporal course of audio-visual object processing.

Multisensory objects that are frequently encountered in the natural environment lead to strong associations across a distributed sensory cortical network, with the end result experience of a unitary percept. Remarkably little is known, however, about the cortical processes sub-serving multisensory object formation and recognition. To advance our understanding in this important domain, the present study investigated the brain processes involved in learning and identification of novel visual-auditory objects. Specifically, we introduce and test a rudimentary three-stage model of multisensory object-formation and processing. Thirty adults were remotely trained for a week to recognize a novel class of multisensory objects (3D shapes paired to complex sounds), and high-density event related potentials (ERPs) were recorded to the corresponding unisensory (shapes or sounds only) and multisensory (shapes and sounds) stimuli, before and after intensive training. We identified three major stages of multisensory processing: 1) an early, multisensory, automatic effect (<100 ms) in occipital areas, related to the detection of simultaneous audiovisual signals and not related to multisensory learning 2) an intermediate object-processing stage (100-200 ms) in occipital and parietal areas, sensitive to the learned multi-sensory associations and 3) a late multisensory processing stage (>250 ms) that appears to be involved in both object recognition and possibly memory consolidation. Results from this study provide support for multiple stages of multisensory object learning and recognition that are subserved by an extended network of cortical areas.

System for neural network recognition of malignant pigmented skin neoplasms with image pre-processing

The article presents a system for the recognition of malignant pigmented skin neoplasms with a preliminary processing stage. Image pre-processing consists of removing hair structures from images, as well as resizing images and their further augmentation. Augmentation made it possible to increase the variety of training data, balance the number of images in different categories, and avoid retraining the neural network. The modeling was carried out using the MatLab R2020b software package for solving technical calculations on clinical dermatoscopic images from the international open archive ISIC Melanoma Project. The proposed system for the recognition of malignant pigmented skin neoplasms made it possible to increase the accuracy of image classification up to 80.55%. The use of the proposed recognition system will make it possible to increase the efficiency and quality of diagnosis, in comparison with the methods of visual diagnosis.