Classifying Highways: Hierarchical Grouping versus Kohonen Neural Networks

The COVID-19 pandemic is recognized as one of the most serious global health problems, and many countries implemented lockdown measures to mitigate the effects of the crisis caused by this respiratory infectious disease. In this study, we investigated the relationship between social distancing policies and changes in traffic volume in Sinchon Station, South Korea. We used an official COVID-19 report provided by the Korea Disease Control and Prevention Agency (KCDA) and Seoul Metropolitan Government (SMG) to review social distancing policies, and the changes in traffic patterns before and during the COVID-19 pandemic between January 2020 and November 2021 were analyzed. Our study reveals that the changes in the overall traffic patterns from acceleration phases to deceleration phases of COVID-19 were related to the alert levels of social distancing policies implemented to tackle the situation resulting from the COVID-19 pandemic. Herein, we found that a significant decline in traffic volume took place from August to September 2020 (13.5–19.7%, weekday; 19.4–31.7%, weekend), from December 2020 to January 2021 (20.0%−26.6%, weekday; 26.8–34.0%, weekend), and from July to September 2021 (3.2–13.1%, weekday; 38.3–44.7%, weekend) when compared to the corresponding periods in 2019 (paired t-test; p < 0.001). The results of this study provide strong support for the effectiveness of Seoul’s preemptive measures, namely, the central government’s intensive social distancing campaign, in managing and reducing the impact of the pandemic situation based on the precise analysis of 10 types of facilities.

One of the important phases in Automatic License Plate Recognition (ALPR) is the recognition or decoding of license plate characters from segmented individual character images in to ASCII characters. The accuracy of the character recognition determines ultimately the performance of the system. Since the ALPR receives images from live traffic, a reconfigurable system is the best option for character recognition. In this paper, LP character recognition is attempted using the Kohonen Neural Network (KNN) which differs from the feed forward back propagation ANN neural network interms of how it is trained and how it recalls a pattern. The KNN does not use any sort of activation function or does not use any sort of a bias weight; the output from the KNN does not consist of the output of several neurons. When a pattern is presented to a KNN one of the output neurons is selected as a "winner". This ‘winning’ neuron is the output from the KNN. KNN consists of interconnected processing elements called neurons that work together to produce a LP ASCII Character output. The LP OCR system learns characters through training process.

Although reinforcement learning (RL) shows advantages in cellular network load balancing, it suffers from a low generalization ability, preventing it from real-world applications. Specifically, if network traffic pattern changes, the learned RL policy cannot adapt accordingly, resulting in system performance degradation. To address this issue, we propose a Multi-teacher MOdel BAsed Reinforcement Learning algorithm (MOBA), which leverages multi-teacher knowledge distillation theory to learn a generalized load balancing policy for adapting the real-world traffic pattern changes. The key is that different teachers represent different traffic patterns, and can learn various system models. By distilling and transferring the teacher knowledge, the student network is able to learn a generalized system model that covers different traffic patterns and unseen situations. Moreover, to improve the robustness of multi-teacher knowledge transfer, we learn a set of student models and use an ensemble method to jointly predict system dynamics. Results show that, compared with state-of-the-art RL methods, MOBA improves the minimal throughput and total throughput of a cellular network by up to 28.6% and 23.2%. Results also show that MOBA improves the training efficiency by up to 64%.

The faults in switched reluctance motors (SRMs) were detected and diagnosed in real time with the Kohonen neural network. When a fault happens, both financial losses and undesired situations may occur. For these reasons, it is important to detect the incipient faults of SRMs and to diagnose which faults have occurred. In this study, a test rig was realized to determine the healthy and faulty conditions of SRMs. A data set for the Kohonen neural network was created with implemented measurements. A graphical user interface (GUI) was created in Matlab to test the performance of the Kohonen artificial neural network in real time. The data of the SRM was transferred to this software with a data acquisition card. The condition of the motor was monitored by marking the data measured in real time on the weight position graph of the Kohonen neural network. This test rig is capable of real-time monitoring of the condition of SRMs, which are used with intermittent or continuous operation, and is capable of detecting and diagnosing the faults that may occur in the motor. The Kohonen neural network used for detection and diagnosis of faults of the SRM in real time with Matlab GUI was embedded in an STM32 processor. A prototype with the STM32 processor was developed to detect and diagnose the faults of SRMs independent of computers.

Development of network cybersecurity technology needs IDS on network technology because modern networks face against dynamic attacks which display various attack methods. To address this problem, we present a different combination of a hybrid ideal solution for an adaptive intrusion detection framework that is incorporated with various analytical techniques approach to achieve accuracy and reliability in detecting threats. This proposed ideal design combines Random Forest based noise-handling classifier and recognizing structured and also static traffic behaviour, while the LSTM neural model helps to analyze traffic behavior by understanding temporal patterns. Security professionals can track network-based attack behavior through their ability to monitor traffic pattern changes which occur over time. The forecast detector system detects unusual network traffic patterns which the fusion model layer uses to create precise threat responses. The framework receives validation through testing with the CICIDS2017 is the dataset which contains various type of traffic patterns from both normal and attack activities. It performs comprehensive data preprocessing, feature selection and normalization to get better input data quality. The model becomes more adaptable through pseudo-labeling which enables it to analyze and learn from unclassified network traffic while utilizing this each set of information for subsequent training cycles to detect new traffic patterns. The system performs continuous network behavior monitoring through its clustering with retraining process. The method demonstrates that uniting statistical analysis with temporal data and adaptive learning into one system framework produces better intrusion detection results which also enable detecting intrusion for network at large scales.

Wireless devices operating under IEEE 802.11 x standards have the ability to transmit at various data-rates. However, there is no specification in the standard for dynamically selecting a suitable value of data-rate. Numerous solutions are available, mainly from device manufacturers and research community; focusing on a particular algorithm and its suitability for the overall system. However, none of the previous designs were flexible to change their behavior with changes in traffic nature and pattern. Not all traffic types have the same requirements and so should be the behavior of underlying algorithms for data-rate control. This paper highlights various data-rate control algorithms; classifying them on the basis of several parameters and discusses their suitability for different traffic patterns. It also proposes an algorithm, specifically designed for real time services. The cross-layer framework monitors traffic nature and patterns at the transport layer and activates one of three available rate control algorithms which suits the requirements of a particular type of traffic. According to the specific design of a selected algorithm, it then selects data-rate values at runtime. The results verify the tremendous difference among the three algorithms in terms of throughput and delay and signify their usability for specific traffic types.

ABSTRACTPeppers (Capsicum annuum L.) are a vegetable that is widely cultivated in various regions of the world. Despite the economic importance of peppers, their commercialization is hindered by their limited postharvest durability, primarily due to moisture loss during storage. This study evaluated the effectiveness of different packaging methods and storage conditions in preserving the physicochemical and morphological quality of peppers during 21 days. Six treatments were tested, combining two types of packaging (thermo‐sealable and macro‐perforated) with two storage conditions (8°C/95% RH and 25°C/60% RH), plus an unpackaged control. Variables assessed included color, soluble solids, pH, pigments, dimensions, and mass loss. Data were analyzed using principal component analysis (PCA), hierarchical cluster analysis (HCA), and Kohonen neural networks (KNN). The first three principal components (PCs) explained 67.2% of total variance (PC1—40.88%, PC2—15.11%, PC3—11.17%). PC1 was strongly associated with mass and size losses (up to 73%), whereas PC2 and PC3 explained 77.4% of h* and 84.9% of C*, respectively. HCA and KNN revealed similar groupings. Samples stored at 8°C clustered together regardless of packaging, indicating minimal quality loss. At 25°C, unpackaged and macro‐perforated samples showed similar degradation. Thermo‐sealable packaging at 25°C formed a distinct cluster, indicating improved protection. This treatment also showed reduced quality losses, though not as effective as refrigeration. The agreement among PCA, HCA, and KNN confirms the reliability of findings. These results highlight the value of combining conservation strategies with multivariate tools to guide efficient, sustainable postharvest practices and extend shelf life in the pepper supply chain.Practical ApplicationThis study proposes a solution for the horticultural industry by combining heat‐shrink packaging and refrigeration for pepper preservation. This method significantly reduces physical and biochemical losses, extends shelf life, and maintains quality. It has the potential to transform the logistics of production and distribution, delivering fresh, high‐quality peppers. The use of advanced techniques like PCA and neural networks enables more informed and efficient decision‐making, allowing for customized preservation strategies. This approach meets the growing demand for fresh food, offering a sustainable, cost‐effective alternative for postharvest preservation, and may provide a competitive advantage in the global market.

To improve the efficiency of detecting abnormal traffic incidents on the road network and reduce the false alarm rate, a real-time traffic anomaly detection framework based on a graph spatiotemporal pattern learning (GSTPL) network is proposed. In this framework, a traffic pattern search algorithm based on a fluctuation similarity measure is designed to screen traffic flow data with the same traffic pattern, and a traffic pattern graph tuple is constructed as the input of the network model to avoid the sample imbalance problem and the effect of single-sample randomness for traffic pattern learning. Then the GSTPL network is designed to extract, unsupervised, the traffic spatiotemporal pattern features and make a reasonable prediction of future traffic parameters as the basis for anomaly evaluation. To further restrain the effect of random fluctuations in traffic flow parameters, an abnormal state evaluation method is designed to calculate the anomaly state likelihood by prediction error distribution learning. The overall detection framework realizes stable prediction of network key node traffic parameters by using spatiotemporal pattern features to construct the traffic pattern graph tuple, and gives incident evaluation results in real time by combination with the detection data. The experiment uses I90 and I405 highway traffic data in Seattle, WA, from 2015. Through comparative analysis, the proposed incident detection method based on GSTPL has a higher detection rate and lower false alarm rate, can adaptively learn dynamic changes of the traffic pattern, and has strong adaptability and stability to different traffic environments.

5G resources should be properly planned for users to have a good quality of service. This planning includes defining the most suitable numerology indexes and best spectrum allocation considering the requirements of current traffic, which may change over time. Furthermore, when accounting for changes in traffic pattern, any necessary reconfigurations should be minimized. Here in this article, an optimization model is developed for the planning of spectrum allocation to the best mix numerology. The model considers adapting the operating numerology mix according to the current presence (or not) of traffic requirements. The model also works under any wireless communication scenario in 5G, and under any traffic pattern.

BackgroundThe Democratic National Convention (DNC) in Boston, Massachusetts in 2004 provided an opportunity to evaluate the impacts of a localized and short-term but potentially significant change in traffic patterns on air quality, and to determine the optimal monitoring approach to address events of this nature. It was anticipated that the road closures associated with the DNC would both influence the overall air pollution level and the distribution of concentrations across the city, through shifts in traffic patterns.MethodsTo capture these effects, we placed passive nitrogen dioxide badges at 40 sites around metropolitan Boston before, during, and after the DNC, with the goal of capturing the array of hypothesized impacts. In addition, we continuously measured elemental carbon at three sites, and gathered continuous air pollution data from US EPA fixed-site monitors and traffic count data from the Massachusetts Highway Department.ResultsThere were significant reductions in traffic volume on the highway with closures north of Boston, with relatively little change along other highways, indicating a more isolated traffic reduction rather than an across-the-board decrease. For our nitrogen dioxide samples, while there was a relatively small change in mean concentrations, there was significant heterogeneity across sites, which corresponded with our a priori classifications of road segments. The median ratio of nitrogen dioxide concentrations during the DNC relative to non-DNC sampling periods was 0.58 at sites with hypothesized traffic reductions, versus 0.88 for sites with no changes hypothesized and 1.15 for sites with hypothesized traffic increases. Continuous monitors measured slightly lower concentrations of elemental carbon and nitrogen dioxide during road closure periods at monitors proximate to closed highway segments, but not for PM2.5 or further from major highways.ConclusionWe conclude that there was a small but measurable influence of DNC-related road closures on air quality patterns in the Boston area, and that a low-cost monitoring study combining passive badges for spatial heterogeneity and continuous monitors for temporal heterogeneity can provide useful insight for community air quality assessments.

Operating room (OR) human traffic has been implicated as a cause of surgical site infection. We first observed the normal human traffic pattern in our Pediatric Orthopedic ORs, and then examined the effect of surveillance on that traffic pattern. This study consisted of 2 phases: phase I sought to observe the OR traffic pattern (number of door swings, maximum and minimum number of OR personnel, number of OR personnel at 30-minute intervals, or changes in nursing, anesthesia, or surgeon staff) during surgical cases without OR personnel being notified, and for phase II, the same traffic pattern was monitored with their knowledge. Two thousand four hundred forty-two minutes of surgical time were observed in phase I, and 1908 minutes were observed in phase II. There was no difference (P=0.06) in the time between door swings between phase I (1.39 min) and phase II (1.70 min), no difference (P=1.000) in the maximum number of people in the OR between phase I (11.5 people, range: 7-15 people) and phase II (11.5 people, range: 8-20 people), and no difference (P=1.000) in the minimum number of people in the OR between phase I (4.67 people, range: 4-6 people) and phase II (4.71 people, range: 3-6 people). There was a difference in the time between door swings (P=0.03) and maximum number of people in the OR (P=0.005) based on the length of the surgery (less or more than 120 min). There was no difference in the time between door swings (P=0.11), but there was a difference in the maximum number of people in the OR (P=0.002) based on type of surgery (spine vs. others). There was no role of surveillance of human traffic in the OR. To achieve any change in the OR traffic pattern, monitoring alone may not be sufficient; other novel techniques or incentives may need to be considered.

In this paper we present analog current mode Euclidean distance calculation (EDC) block, which calculates the distance between two current vectors. The proposed circuit is an important part of the CMOS-implemented Kohonen’s neural network (KNN) designed for medical applications. The input data vector is compared with the weights’ vector in each neuron in proposed KNN. The neuron, whose weights are the closest to the input training vector becomes the winner and in the next step changes its weights. Proposed EDC block performs several operations such as: subtraction, squaring and summing of the current signals. The output current is the exact measure of the Euclidean distance. Proposed circuit dissipates power 15 μW from 1.5 V voltage supply, working with 20 MHz input data frequency. The signal frequency as well as the power dissipation may be scaled down to 1 MHz and 300 nA. Proposed EDC circuit, in CMOS 0.18 μm technology, occupies area about 500 μm2.

Infertility is a global problem affecting 48 to 186 million couples of reproductive age. In Poland, it concerns approx. 1.5 million couples, which amounts to 20% of the population capable of reproducing. One of the factors influencing the incidence of fertility disorders may be lifestyle, understood as a multi-disciplinary accumulation of everyday behaviours and habits. In the study, a group of 201 young adults, students of medical and related faculties, were surveyed in order to check the actual level of knowledge about the impact of lifestyle on reproductive health. The Kohonen network, which is an example of a self-learning neural network, was used to find non-obvious connections between the data. The trained Kohonen neural network formed 4 clusters with different characteristics. Based on analyses of the structure of each cluster, it was found that 2nd year students of Medicine are internally divided into 3 fractions. The first fraction declared a high level of knowledge, but did not have real knowledge. The second fraction was aware of their ignorance, as confirmed by the knowledge test. The last fraction was characterized by a high level of self-confidence regarding their knowledge about reproductive health and obtained a high result in the knowledge test. It was confirmed that people studying at the Medical faculty know more than students of the same year at faculties other than Medicine. Interesting results were obtained for a group of 3rd year students of first-cycle studies in Dietetics. They did not obtain a significantly better result in the knowledge test concerning the influence of diet and lifestyle on reproductive health. It would seem that one could expect at least a few highly knowledgeable students in a group of 3rd year students, but this was not confirmed by the study. In view of the obtained results, it was concluded that the Kohonen neural network is applicable to the analysis of data on the actual state of knowledge about the impact of lifestyle on reproductive health.

Regional flood frequency analysis for the Gan-Ming River basin in China

The double‐deck elevator system (DDES) has been invented firstly as a solution to improve the transportation capacity of elevator group systems in the up‐peak traffic pattern. The transportation capacity could be even doubled when DDES runs in a pure up‐peak traffic pattern where two connected cages stop at every two floors in an elevator round trip. However, the specific features of DDES make the elevator system intractable when it runs in some other traffic patterns. Moreover, since almost all the traffic flows vary continuously during a day, an optimized controller of DDES is required to adapt to the varying traffic flow. In this paper, we have proposed a controller adaptive to traffic flows for DDES using Genetic Network Programming (GNP) based on our past studies in this field, where the effectiveness of DDES controller using GNP has been verified in three typical traffic patterns. A new traffic flow judgment part was introduced into the GNP framework of DDES controller in this paper, and the different parts of GNP were expected to be functionally localized by the evolutionary process to make the appropriate cage assignment in different traffic flow patterns. Simulation results show that the proposed method outperforms a conventional approach and two heuristic approaches in a varying traffic flow during the working time of a typical office building. Copyright © 2008 Institute of Electrical Engineers of Japan. Published by John Wiley &amp; Sons, Inc.