Hasil untuk "Computer software"

J. D'haese, Johan Ackhurst, D. Wismeijer et al.

Gianluca Aguzzi, Roberto Casadei, Mirko Viroli

Swarm behaviour engineering is an area of research that seeks to investigate methods and techniques for coordinating computation and action within groups of simple agents to achieve complex global goals like pattern formation, collective movement, clustering, and distributed sensing. Despite recent progress in the analysis and engineering of swarms (of drones, robots, vehicles), there is still a need for general design and implementation methods and tools that can be used to define complex swarm behaviour in a principled way. To contribute to this quest, this article proposes a new field-based coordination approach, called MacroSwarm, to design and program swarm behaviour in terms of reusable and fully composable functional blocks embedding collective computation and coordination. Based on the macroprogramming paradigm of aggregate computing, MacroSwarm builds on the idea of expressing each swarm behaviour block as a pure function, mapping sensing fields into actuation goal fields, e.g., including movement vectors. In order to demonstrate the expressiveness, compositionality, and practicality of MacroSwarm as a framework for swarm programming, we perform a variety of simulations covering common patterns of flocking, pattern formation, and collective decision-making. The implications of the inherent self-stabilisation properties of field-based computations in MacroSwarm are discussed, which formally guarantee some resilience properties and guided the design of the library.

TAN Ruoqi, DONG Minggang, ZHAO Weixiao, WU Tianhao

Holding non-motorized vehicles accountable for legal violations effectively enhances urban traffic safety. Non-motorized vehicle license plates are characterized by small size, dense distribution, and ease of being obscured, which leads to significant feature information loss during the detection process in traditional deep learning-based methods. A non-motorized vehicle license plate recognition and localization method based on semantic alignment and hierarchical optimization is proposed. In this method, a semantic alignment module is designed for the underlying information fusion. During the upsampling process, low-level target information is used to guide the fusion of high-level semantics downwards, addressing the loss of small target features caused by conflicts between high- and low-level semantics. Subsequently, a hierarchical optimization module is constructed within the CSP structure to replace the deep ELAN module. This module uses a stack of a few convolutional kernel modules to extract the target information, reducing the number of network layers and preventing the loss of feature information at deeper levels. In the final stage, the K-Means++ algorithm is employed to cluster and obtain the initial anchor boxes suitable for non-motorized license plates to reduce the matching error during the training process. This approach aims to improve the accuracy of small-object recognition and localization. The experimental results demonstrate that the proposed method achieves a recognition and localization accuracy of 90.95% on a non-motorized vehicle license plate dataset. Compared with representative methods such as YOLOv7 and YOLOv8, it improves the accuracy by at least 3.58%. The proposed approach is effective for non-motorized vehicle license plate recognition and localization.

Xuehui Li, Pan Tang, Xinping Pang et al.

Abstract Alzheimer's disease (AD) is a neurodegenerative disorder characterized primarily by cognitive impairment. The motivation of this paper is to explore the impact of the visual information transmission pathway (V–H pathway) on AD, and the following feature were observed: Hemoglobin expression on the V–H pathway becomes dysregulated as AD occurs so as to the pathway becomes dysfunctional. According to the feature, the following conclusion was proposed: As AD occurs, abnormal tau proteins penetrate bloodstream and arrive at the brain regions of the pathway. Then the tau proteins or other toxic substances attack hemoglobin molecules. Under the attack, hemoglobin expression becomes more dysregulated. The dysfunction of V–H pathway has an impact on early symptoms of AD, such as spatial recognition disorder and face recognition disorder.

Raenald Syaputra, Taghfirul Azhima Yoga Siswa, Wawan Joko Pranoto

Banjir merupakan salah satu bencana alam yang sering terjadi di Indonesia, termasuk di Kota Samarinda dengan 18-33 titik desa terdampak dari tahun 2018-2021. Penggunaan machine learning dalam mengklasifikasi bencana banjir sangat penting untuk memprediksi kejadian di masa mendatang. Beberapa penelitian sebelumnya terkait klasifikasi data banjir dalam 3 tahun terakhir telah dilakukan. Namun, dari beberapa penelitian tersebut memunculkan masalah terkait dengan dataset high dimensional yang dapat menurunkan performa model klasifikasi dan menyebabkan overfitting. Selain itu, masalah lain juga muncul dalam hal imbalance data yang menyebabkan bias terhadap kelas mayoritas dan representasi yang tidak akurat. Oleh karena itu, permasalahan dataset high dimensional dan imbalance data merupakan tantangan spesifik yang harus diatas dalam klasifkasi data banjir Kota Samarinda. Penelitian ini bertujuan mengidentifkasi fitur-fitur yang diperoleh dari seleksi fitur Genetic Algorithm (GA) yang memiliki pengaruh terhadap akurasi klasifikasi data banjir Kota Samarinda menggunakan algoritma Support Vector Machine (SVM), serta meningkatkan akurasi klasifikasi data banjir di Kota Samarinda dengan mengimplementasikan algoritma SVM yang dikombinasikan dengan metode Synthetic Minority Oversampling Technique (SMOTE) untuk oversampling, seleksi fitur dengan GA dan optimasi menggunakan Particle Swarm Optimization (PSO). Teknik validasi yang digunakan adalah 10-fold cross validation dan evaluasi performa menggunakan confusion matrix. Data yang digunakan berasal dari BPBD (Badan Penanggulangan Bencana Daerah) dan BMKG (Badan Meteorologi, Klimatologi, dan Geofisika) Kota Samarinda pada tahun 2021-2023 terdiri dari 11 fitur dan 1.095 record. Hasil penelitian menunjukkan bahwa fitur-fitur penting yang terpilih melalui GA adalah temperatur maksimum, kecepatan angin maksimum, arah angin maksimum, arah angin terbanyak, lamanya penyinaran matahari dan kecepatan angin rata-rata. Dengan kombinasi metode SVM, SMOTE, GA dan PSO, akurasi klasifikasi data banjir mencapai 82,28%. Namun, penelitian ini juga menghadapi tantangan seperti kontradiksi hasil dengan penelitian lain terkait penggunaan SMOTE dan variasi hasil akibat karakteristik dataset serta metode pembagian data yang berbeda. Hasil penelitian ini dapat digunakan oleh pemerintah daerah dan badan penanggulangan bencana daerah Kota Samarinda untuk memprediksi kejadian banjir dengan lebih akurat, serta memungkinkan tindakan pencegahan yang lebih efektif. Penerapan hasil penelitian ini dapat meningkatkan efektivitas dalam mitigasi bencana banjir Kota Samarinda.

Tanuja Shailesh, Ashalatha Nayak, Devi Prasad

Verification of real-time system properties using formal models can improve system design and quality. The Timed Petri net is a formal model for modelling and designing real-time systems with time constraints. Furthermore, model checking is a formal verification method used to verify system properties using model checkers. This article proposes an automated transformation system for mapping a Timed Petri net into one of the common model checkers, the SPIN PROMELA model, to verify real-time system properties. This approach enables the combination of two modelling paradigms and supports system verification using system design models. In this study, the system properties were verified using both the simulation method and linear temporal logic formulas supported by the SPIN model checker. Timed Petri net models of two different case studies, a central server computer system and a manufacturing Kanban system were considered to verify the boundedness, liveness and system behavioral properties using the proposed transformation system.

Cameron J. Hargreaves, Michael W. Gaultois, Luke M. Daniels et al.

Abstract The application of machine learning models to predict material properties is determined by the availability of high-quality data. We present an expert-curated dataset of lithium ion conductors and associated lithium ion conductivities measured by a.c. impedance spectroscopy. This dataset has 820 entries collected from 214 sources; entries contain a chemical composition, an expert-assigned structural label, and ionic conductivity at a specific temperature (from 5 to 873 °C). There are 403 unique chemical compositions with an associated ionic conductivity near room temperature (15–35 °C). The materials contained in this dataset are placed in the context of compounds reported in the Inorganic Crystal Structure Database with unsupervised machine learning and the Element Movers Distance. This dataset is used to train a CrabNet-based classifier to estimate whether a chemical composition has high or low ionic conductivity. This classifier is a practical tool to aid experimentalists in prioritizing candidates for further investigation as lithium ion conductors.

Hezam Albaqami, Ghulam Mubashar Hassan, Amitava Datta

Neurological disorders have an extreme impact on global health, affecting an estimated one billion individuals worldwide. According to the World Health Organization (WHO), these neurological disorders contribute to approximately six million deaths annually, representing a significant burden. Early and accurate identification of brain pathological features in electroencephalogram (EEG) recordings is crucial for the diagnosis and management of these disorders. However, manual evaluation of EEG recordings is not only time-consuming but also requires specialized skills. This problem is exacerbated by the scarcity of trained neurologists in the healthcare sector, especially in low- and middle-income countries. These factors emphasize the necessity for automated diagnostic processes. With the advancement of machine learning algorithms, there is a great interest in automating the process of early diagnoses using EEGs. Therefore, this paper presents a novel deep learning model consisting of two distinct paths, WaveNet–Long Short-Term Memory (LSTM) and LSTM, for the automatic detection of abnormal raw EEG data. Through multiple ablation experiments, we demonstrated the effectiveness and importance of all parts of our proposed model. The performance of our proposed model was evaluated using TUH abnormal EEG Corpus V.2.0.0. (TUAB) and achieved a high classification accuracy of 88.76%, which is higher than in the existing state-of-the-art research studies. Moreover, we demonstrated the generalization of our proposed model by evaluating it on another independent dataset, TUEP, without any hyperparameter tuning or adjustment. The obtained accuracy was 97.45% for the classification between normal and abnormal EEG recordings, confirming the robustness of our proposed model.

Jian HUANG, Xin LI, Fang CHEN et al.

The traditional high-level remote landslide recognition efficiency which relies on the artificial discrimination of geological experts is low. In this paper, an automatic landslide terrain recognition model based on deep learning is developed to improve the efficiency of the screening of potential landslide hazard in a large area. The model takes remote sensing images, DEM data, geological zones, river system and other geological observation data of the target area as input. For the huge difference of different types of observation data, a feature branch network is designed and constructed to accurately extract the corresponding landslide features: Among them, deep network architecture is used to extract complex features from optical image data, and shallow network architecture is used to extract features from structured data such as altitude, geological composition, river and fault zone distribution. Subsequently, a feature fusion module was designed to fuse the extraction results of the two networks to obtain a comprehensive landslide hazard feature. The model performs semantic segmentation of the landslide area based on the extracted landslide features, and achieves accurate pixel-level landslide terrain classification and positioning. The experimental results show that the recognition accuracy(ACC) of the model reaches 0.85, which can provide technical support for automatic landslide identification.

Hongyan Xu, Ayten Yiğiter, Jie Chen

Change point analysis has been useful for practical data analytics. In this paper, we provide a new R package, onlineBcp, based on an online Bayesian change point detection algorithm. This R package conveniently outputs the maximum posterior probabilities of multiple change points, loci of change points, basic statistics for segments separated by identified change points, confidence interval for each unknown segment mean and a plot displaying the segmented data. Practically, missing value pre-treatment of the data, before the change point detection algorithm is implemented, is built in this package. In addition, the Kolmogorov–Smirnov test for checking the normality assumption on each segment, post-change point detection, is included as an option in the package for the ease of data analytic and assumption checking flow. When additional data come in, the package provides a function to combine changes identified based on prior data and changes identified based on additional data and thus provides a fast detection of change points in the data stream when new batches of data are collected.

Youngmi Baek, Euiguk Jung

This study investigates the start-up and steady-state heat transfer performance of a heat pipe with a bypass line for accelerating a working fluid. The interface resistance by the counterflow of vapor and liquid under the operation of a heat pipe has a significant effect on the heat transfer performance. An experimental study was conducted on the thermal performance improvement of a heat pipe, which was induced by a reduction in the flow resistance above the phase interface with a counterflow. A heat pipe was connected to the evaporator start and condenser end with a liquid bypass line. The liquid bypass line was designed to improve the steady-state heat transfer performance of the heat pipe by bypassing a portion of the liquid inside the condenser to the evaporator without passing through the capillary structure. Acetone was used as the working fluid, and the effect of the bypass line on the heat transfer performance of the heat pipe was experimentally investigated. The input thermal load, coolant temperature, and inclination of the heat pipe were selected as the experimental variables. The heat transfer performance of the heat pipe was evaluated by the thermal resistance, and the normal operation mode and bypass operation mode were quantitatively compared. When the bypass line was used, the thermal resistance of the heat pipe decreased by up to a maximum of 61%.

Jean Pierre Uwiringiyimana, Umar Khayam, Suwarno et al.

This article presents a design of ultra-high frequency (UHF), ultra-wide band (UWB) antenna used for partial discharge (PD) detection on high voltage and medium voltage power system equipment. The proposed UHF antenna has a working frequency band of 1.2GHz-4.5GHz, covering a total bandwidth of 3.3GHz with a return loss of less than -10dB in the entire antenna&#x2019;s operating frequency. The computer simulation technology (CST) Microwave Studio software was used to design, simulate and optimize the proposed antenna. Upon simulation and optimization process, the antenna prototype was fabricated on the FR-4 substrate of 1.6 mm thickness and dielectric permittivity of 4.4. This antenna has a compact size of 100mm <inline-formula> <tex-math notation="LaTeX">$\times100$ </tex-math></inline-formula>mm.The radiating patch and the ground plane of this antenna are made of annealed copper whose thickness is 0.035mm. The simulations and measurement results for the proposed antenna are in a good agreement, and the return loss of this antenna is less than &#x2212;10dB with voltage standing wave ratio, VSWR, &#x003C; 2 within the frequency range of interest. The proposed antenna performance in PD sensing is compared with a commercial high-frequency current transformer, HFCT. To validate the sensitivity performance of the designed antenna, experimental PD measurements were carried out by using an epoxy slab inserted between two parallel plates electrode model, in order to generate surface discharge on the insulator, and using a needle-plate electrode configuration to generate corona discharge in transformer oil. Based on PD measurement results, it was shown that the designed antenna has a high sensitivity, which make it a suitable candidate for UHF partial discharge monitoring on high voltage and medium voltage power assets.

Qun Zhao, Nisuo Du, Zhi Ouyang et al.

Abstract Person re‐identification (Re‐ID) is in significant demand for intelligent security and single or multiple‐target tracking. However, there are issues in the person Re‐ID tasks, such as sharp decline in cross‐data sets detection accuracy, poor generalization and cross‐domain ability of the model. This work mainly studies the generalization and adaptation of cross‐domain person Re‐ID models. Different from most existing methods for cross‐domain Re‐ID tasks, the authors use diversified spatial semantic feature in pixel‐level learning in the target domain to improve the generality and adaptability of the model. In the case that no information of the target domain is used during the model training, the trained model is directly tested on the data set of the target domain. It has proven effective to add the attention cascade module into the backbone network combining with the part‐level branch. The authors conducted extensive experiments based on the three data sets of Market‐1501, DukeMTMC‐ReID and MSMT17, resulting in both single‐domain and cross‐domain tests with an average improvement of Rank1 and mAP values of about 10% compared with Baseline through the authors' proposed method named Part‐Level Attention Network.

Shilei Xue, Zhao Hao, Ying An et al.

The objectification of pulse diagnosis is very important to the development and inheritance of TCM, the first step is how to collect more abundant and comprehensive pulse information quickly, reduce the threshold of users for using pulse diagnosis equipment. The existing pulse diagnosis equipment has some limitations, such as single acquisition site, complex compression form and serious dependence on professionals for correcting-pulse position selection. Therefore, a three-pulse diagnosis system based on flexible sensor is designed, which uses a new type of flexible sensor as the data acquisition port, combined with upper computer software and lower computer software to achieve goals of intelligent decompression and data acquisition from Cun, Guan, Chi. The equipment not only greatly reduces the difficulty for users to find correct pulse position identification, but also collect non-destructive pulse information, which provides a new acquisition mode for the pulse diagnosis instrument.

Long Zhang, Chuang Zhu, YueWei Wu et al.

Abstract Ischemic stroke is the most common stroke and the leading cause of disability and death in the world. Computed tomography (CT) is a popular and economical diagnostic device for the stroke, However the ischemic stroke lesions are not evident on CT images and the diagnostic result relies on the visual observation of neurologists, which may vary from doctor to doctor. To facilitate the treatment, a computer‐aided detection algorithm on CT images is proposed to help clinician for the ischemic stroke screening. In order to obtain accurate lesion annotation on CT images, novel automatic algorithms are developed to achieve image pairing, calibration, and registration. Then, a new framework with the symmetric feature extraction and comparison is proposed to identify and locate the ischemic stroke lesion. Experimental results show that this method achieves 75% of DICE in the detection of ischemic stroke lesions, which is higher than other methods by 4%. Its competitive results compared with seven latest methods is shown in terms of extensive qualitative and quantitative evaluation. This method can accurately detect the lesion in the CT images through the comparison of symmetric regional features, which has contributed to the clinical diagnosis of ischemic stroke.

Jialiang Wang, Jianxu Luo, Bin Liu et al.

In this work, we develop a computer‐aided retinal image screening system that can perform automated diabetic retinopathy (DR) grading and DR lesion detection in retinal fundus images. We propose a modified object‐detection method for this task via a region‐based fully convolutional network (R‐FCN). A feature pyramid network and a modified region proposal network are applied to enhance the detection of small objects. The DR‐grading model based on the modified R‐FCN is evaluated on the Messidor data set and images provided by the Shanghai Eye Hospital. High sensitivity of 99.39% and specificity of 99.93% are obtained on the hospital data. Moreover, high sensitivity of 92.59% and specificity of 96.20% are obtained on the Messidor data set. The modified R‐FCN lesion‐detection model is validated on the hospital data set and achieves a 92.15% mean average precision. The proposed R‐FCN can efficiently accomplish DR grading and lesion detection with high accuracy.

Shah Nazir, Sulaiman Khan, Habib Ullah Khan et al.

Healthcare systems are transformed digitally with the help of medical technology, information systems, electronic medical records, wearable and smart devices, and handheld devices. The advancement in the medical big data, along with the availability of new computational models in the field of healthcare, has enabled the caretakers and researchers to extract relevant information and visualize the healthcare big data in a new spectrum. The role of medical big data becomes a challenging task in the form of storage, required information retrieval within a limited time, cost efficient solutions in terms care, and many others. Early decision making based healthcare system has massive potential for dropping the cost of care, refining quality of care, and reducing waste and error. Scientific programming play a significant role to overcome the existing issues and future problems involved in the management of large scale data in healthcare, such as by assisting in the processing of huge data volumes, complex system modelling, and sourcing derivations from healthcare data and simulations. Therefore, to address this problem efficiently a detailed study and analysis of the available literature work is required to facilitate the doctors and practitioners for making the decisions in identifying the disease and suggest treatment accordingly. The peer reviewed reputed journals are selected for the accumulated of published research work during the period ranges from 2015 - 2019 (a portion of 2020 is also included). A total of 127 relevant articles (conference papers, journal papers, book section, and survey papers) are selected for the assessment and analysis purposes. The proposed research work organizes and summarizes the existing published research work based on the research questions defined and keywords identified for the search process. This analysis on the existence research work will help the doctors and practitioners to make more authentic decisions, which ultimately will help to use the study as evidence for treating patients and suggest medicines accordingly.

Mohamed O. Elsedfy, Wael A. Murtada, Ezz F. Abdulqawi et al.

Nowadays, virtualization and real-time systems are increasingly relevant. Real-time virtual machines are adequate for closely-coupled computer systems, execute tasks from associated language only and re-target tasks to the new platform at runtime. Complex systems in space, avionics, and military applications usually operate with Loosely-Coupled Computer Systems in a real-time environment for years. In this paper, a new approach is introduced to support task transfer between loosely-coupled computers in a real-time environment to add more features without software upgrading. The approach is based on automatic source code transformation into a platform-independent “Structured Byte-Code” (SBC) and a real-time virtual machine (SBC-RVM). Unlike Ordinary virtual machines which virtualize a specific processor for a specific code only, SBC-RVM transforms source code from any language with a known grammar into SBC without re-targeting the new platform. SBC-RVM executes local or placed tasks and preserving real-time constraints and adequate for Loosely-coupled computer systems.

XU Tengteng,HUANG Hengjun

Supervised-Latent Dirichlet Distribution Allocation (s-LDA) model cannot handle the multi-label problem and s-LDA model is not correct distribution in the classification model.The Supervised Labled-LDA(sl-LDA) model is proposed by adding a category label based on the response variable.It analyses s-LDA model and existed problem of topic classification,through verifying the classification accuracy of sl-LDA model,the paper classifies the sl-LDA model and s-LDA model.Experimental results in the Chinese and English news corpus show that English corpus classification performance is improved by about 3.80% and Chinese corpus is improved by about 1.77%.

LIANG Yulong,QU Dan,QIU Zeyu

The performance of speaker aware training method based on i-vector is poor because of using MFCC which has the relative poor robustness as the input feature for the extraction of the i-vector.To solve this problem,an improved i-vector based speaker aware training method is proposed.Firstly,a low dimensional feature extraction method based on SVD is proposed,and then the feature extracted by this method is used to replace the MFCC,which can extract better i-vector.Experimental results show that,in the Vystadial_cz corpus,compared with the DNN-HMM speech recognition system and the original i-vector based speaker aware training method,the recognition performance of this method is increased by 1.62% and 1.52% respectively,in the WSJ corpus,the recognition performance of this method is increased by 3.9% and 1.48% respectively.