Test Scenarios Research Articles

Advancing blast fragmentation simulation of RC slabs: A graph neural network approach

Accurate prediction of blast-induced fragmentation in reinforced concrete (RC) structures is pivotal for structural debris hazard assessment in an explosion event. This assessment is essential for designing effective measures for protection of people and structure in the vicinity. While conventional computational techniques are proficient, they are computationally very demanding and may not be readily adaptable in practical applications. This study proposes a novel approach, the Fragment Graph Network (FGN), a specialized variant of graph neural networks (GNNs), to simulate complex interactions between macro-level particles of RC structures subjected to close-in blast loads. The RC structure is discretised into particles, with the model predicting structural responses and fragmentation based on states of these particles. Utilizing an autoregressive prediction mechanism, our model can accurately simulate spatiotemporal responses of RC slabs against blast loads. The efficacy of the model was tested on a dataset consisting of 149 simulated cases, encompassing around 180,000 states each, covering a wide range of blasting scenarios and structural design parameters including TNT charge weight, standoff distance, slab dimension, concrete strength, and reinforcement ratio and layout. Notably, the proposed model delivered accurate predictions, achieving relative errors under 10% and R2 values exceeding 0.93 for both standard and extrapolated test scenarios as compared to the SPH simulations. Remarkably, our model dramatically outpaced traditional numerical approaches, reducing simulation times from hours to minutes on CPUs and seconds on GPUs. This pioneering effort paves the way for efficient and accurate simulations of RC blast fragmentations, offering a rapid alternative to standard numerical approaches.

CIBSE TM54 energy projections I: A case study using quasi-steady state modelling

CIBSE TM54 was recently revised and covers best practice methods to evaluate the operational energy use of buildings. TM54 is a guidance document that can be used for performance evaluation at every stage of the design and construction process, and during the occupied stage, to ensure that long-term operational performance aligns with the design intent. The main performance evaluation principles in TM54 are a step-by-step modelling approach and scenario testing, to improve the robustness of the design proposal calculations. The latest edition of this technical memorandum brings an updated perspective to the modelling approaches, including detailed Heating, Ventilation and Air Conditioning (HVAC) modelling and simulation. It also incorporates more detailed guidance around risks, target setting, scenario testing and sensitivity analysis. A case study approach is used to explore and demonstrate some important aspects described in TM54. TM54 recommends three modelling approaches (aka implementation routes) that a project can follow depending on its scale and complexity: using quasi-steady state tools; dynamic simulation with a template HVAC system; and dynamic simulation with detailed HVAC system modelling. As part of a series of three, this case study provides an application of the first implementation route: modelling using quasi-steady state tools. Practical Application This case study provides detailed guidance on undertaking CIBSE TM54 modelling and projecting design stage building performance. The study covers the interpretation and clarifications of how TM54 can be applied, through the quasi-steady-state modelling tools.

Automated Lane Centering: An Off-the-Shelf Computer Vision Product vs. Infrastructure-Based Chip-Enabled Raised Pavement Markers.

Safe autonomous vehicle (AV) operations depend on an accurate perception of the driving environment, which necessitates the use of a variety of sensors. Computational algorithms must then process all of this sensor data, which typically results in a high on-vehicle computational load. For example, existing lane markings are designed for human drivers, can fade over time, and can be contradictory in construction zones, which require specialized sensing and computational processing in an AV. But, this standard process can be avoided if the lane information is simply transmitted directly to the AV. High definition maps and road side units (RSUs) can be used for direct data transmission to the AV, but can be prohibitively expensive to establish and maintain. Additionally, to ensure robust and safe AV operations, more redundancy is beneficial. A cost-effective and passive solution is essential to address this need effectively. In this research, we propose a new infrastructure information source (IIS), chip-enabled raised pavement markers (CERPMs), which provide environmental data to the AV while also decreasing the AV compute load and the associated increase in vehicle energy use. CERPMs are installed in place of traditional ubiquitous raised pavement markers along road lane lines to transmit geospatial information along with the speed limit using long range wide area network (LoRaWAN) protocol directly to nearby vehicles. This information is then compared to the Mobileye commercial off-the-shelf traditional system that uses computer vision processing of lane markings. Our perception subsystem processes the raw data from both CEPRMs and Mobileye to generate a viable path required for a lane centering (LC) application. To evaluate the detection performance of both systems, we consider three test routes with varying conditions. Our results show that the Mobileye system failed to detect lane markings when the road curvature exceeded ±0.016 m-1. For the steep curvature test scenario, it could only detect lane markings on both sides of the road for just 6.7% of the given test route. On the other hand, the CERPMs transmit the programmed geospatial information to the perception subsystem on the vehicle to generate a reference trajectory required for vehicle control. The CERPMs successfully generated the reference trajectory for vehicle control in all test scenarios. Moreover, the CERPMs can be detected up to 340 m from the vehicle's position. Our overall conclusion is that CERPM technology is viable and that it has the potential to address the operational robustness and energy efficiency concerns plaguing the current generation of AVs.

Deep learning of estuary salinity dynamics is physically accurate at a fraction of hydrodynamic model computational cost

AbstractSalinity dynamics in the Delaware Bay estuary are a critical water quality concern as elevated salinity can damage infrastructure and threaten drinking water supplies. Current state‐of‐the‐art modeling approaches use hydrodynamic models, which can produce accurate results but are limited by significant computational costs. We developed a machine learning (ML) model to predict the 250 mg L−1 Cl− isochlor, also known as the “salt front,” using daily river discharge, meteorological drivers, and tidal water level data. We use the ML model to predict the location of the salt front, measured in river miles (RM) along the Delaware River, during the period 2001–2020, and we compare predictions of the ML model to the hydrodynamic Coupled Ocean–Atmosphere‐Wave‐Sediment Transport (COAWST) model. The ML model predicts the location of the salt front with greater accuracy (root mean squared error [RMSE] = 2.52 RM) than the COAWST model does (RMSE = 5.36); however, the ML model struggles to predict extreme events. Furthermore, we use functional performance and expected gradients, tools from information theory and explainable artificial intelligence, to show that the ML model learns physically realistic relationships between the salt front location and drivers (particularly discharge and tidal water level). These results demonstrate how an ML modeling approach can provide predictive and functional accuracy at a significantly reduced computational cost compared to process‐based models. In addition, these results provide support for using ML models in operational forecasting, scenario testing, management decisions, hindcasting, and resulting opportunities to understand past behavior and develop hypotheses.

Distributed predictive motion planning of automated guided vehicles: serial versus parallel schemes

The article presents two distributed motion planning methods for dynamically coordinating multiple automated guide vehicles (AGVs) in an industrial setting, aiming to enhance their flexibility, robustness and scalability. A predictive motion model is utilized to describe the transport process mathematically as a dynamical system. Subsequently, alternating direction method of multipliers (ADMM)-based decomposition techniques, both serial and parallel, are developed to coordinate the AGVs and mitigate the computational burden. The efficacy of the distributed methods is verified through testing in industrial scenarios, demonstrating that they can significantly improve multi-AGV system transport productivity with minimal computational effort. Notably, the parallel scheme exhibits superior performance in coordinating the AGVs compared to the serial scheme.

MEDUSA: A Dynamic Codec Switching Approach in HTTP Adaptive Streaming

HTTP Adaptive Streaming (HAS) solutions utilize various Adaptive BitRate (ABR) algorithms to dynamically select appropriate video representations, aiming to adapt to fluctuations in network bandwidth. However, current ABR implementations have a limitation in that they are designed to function with one set of video representations, i.e. , the bitrate ladder, which differ in bitrate and resolution, but are encoded with the same video codec. When multiple codecs are available, current ABR algorithms select one of them prior to the streaming session and stick to it throughout the entire streaming session. Although newer codecs are generally preferred over older ones, their compression efficiencies differ depending on the content’s complexity, which varies over time. Therefore, it is necessary to select the appropriate codec for each video segment to reduce the requested data while delivering the highest possible quality. In this paper, we first provide a practical example where we compare compression efficiencies of different codecs on a set of video sequences. Based on this analysis, we formulate the optimization problem of selecting the appropriate codec for each user and video segment (on a per-segment basis in the outmost case), refining the selection of the ABR algorithms by exploiting key metrics, such as the perceived segment quality and size. Subsequently, to address the scalability issues of this centralized model, we introduce a novel distributed plug-in ABR algorithm for Video on Demand (VoD) applications called MEDUSA to be deployed on top of existing ABR algorithms. MEDUSA enhances the user’s Quality of Experience (QoE) by utilizing a multi-objective function that considers the quality and size of video segments when selecting the next representation. Using quality information and segment size from the modified Media Presentation Description (MPD) , MEDUSA utilizes buffer occupancy to prioritize quality or size by assigning specific weights in the objective function. To show the impact of MEDUSA, we compare the proposed plug-in approach on top of state-of-the-art techniques with their original implementations and analyze the results for different network traces, video content, and buffer capacities. According to the experimental findings, MEDUSA shows the ability to improve QoE for various test videos and scenarios. The results reveal an impressive improvement in the QoE score of up to 42% according to the ITU-T P.1203 model (mode 0). Additionally, MEDUSA can reduce the transmitted data volume by up to more than 40% achieving a QoE similar to the techniques compared, reducing the burden on streaming service providers for delivery costs.

Application of Deep Learning on Structure Displacement Measurement and Accuracy Analysis

Displacement serves as a crucial indicator in structural health monitoring within civil engineering, with computer vision being a commonly employed tool due to its cost-effectiveness and convenience. This paper introduces a methodology for tracking feature points computed via computer vision techniques SuperPoint and SuperGlue. Two test scenarios are conducted: a bridge loading test characterized by a low uniform stress state, and a tensile strength test marked by a high stress state. However, a notable drawback of this method is the potential misalignment between the survey point (the point under measurement) and the feature point, leading the errors. To address this issue, a fundamental principle for selecting feature points is presented. In the bridge loading test, the displacement of the survey point is recorded, and the influence of misalignment between survey point and feature point on accuracy is analyzed under low-stress uniform conditions. The findings demonstrate the feasibility of this method for field measurements in civil engineering. Subsequently, a tensile strength test is conducted to assess the influence of different feature point selection criteria on accuracy under high-stress conditions. Concurrently, the DIC software Ncorr is applied to analyze the displacement field of feature points near the survey point. The results highlight the significant influence of selecting feature points within regions of high stress concentration on measurement accuracy.

Enhancing Automated Test Case Generation Through KNN algorithm in Software Testing

In the ever-evolving world of software development, assuring the stability and resilience of software systems remains a vital task. This study aims to tackle this problem by suggesting a novel method for generating automated test cases. It involves incorporating the robust KNN algorithm into the domain of data mining methods. The research intends to exploit previous testing data to detect patterns and trends, increasing the production of effective and targeted test scenarios.
 The literature study highlights the increasing overlap between data mining and software testing, underscoring the need for sophisticated methods to address the requirements of contemporary, ever-changing applications. In this regard, our study presents the KNN algorithm as a new addition to the collection of data mining tools for software testing. The process entails gathering extensive historical testing data, including information on test cases, errors, and the attributes of tested software applications. By using rigorous preprocessing and feature selection techniques, the KNN method is used to examine the correlations between various variables and the probability of flaws. The KNN model is trained and optimized using the dataset, and the resultant predictions are then used in an automated system for generating test cases. The experimental results confirm the efficacy of the suggested method in finding crucial test scenarios and prioritizing test cases according to past fault trends. The practical usefulness and advantages of incorporating the KNN algorithm into the automated test case-generating process are further confirmed by real-world case studies. In conclusion, this study expands the discourse on data mining in software testing by proposing the KNN algorithm as a vital component in automated test case development. By using KNN, testing teams can adjust to the ever-changing characteristics of contemporary applications, leading to enhanced efficiency and effectiveness in testing procedures. The results of this research enhance the continuous development of software quality assurance methods and provide opportunities for additional investigation at the convergence of machine learning and software testing

Optimal design of adaptive model predictive control based on improved GWO for autonomous vehicle considering system vision uncertainty

The tuning issue of the parameters and the system uncertainty represent big challenges in most engineering applications. In this regard, a new tuning approach is developed for adaptive model predictive control (AMPC) of autonomous vehicles (AVs). The proposed control strategy tackles the uncertainty issue of the vision system due to the variation of the velocity, road curvature, time delay, and look ahead distance. The proposed AMPC is designed depending on a new improved grey wolf optimizer (IGWO) algorithm with different learning procedures and fitness-distance balance (FDB) approach to equilibrium the exploration and exploitation manners of the original GWO. This development of GWO based on FDB can increase the capability of the GWO for global search and avoid trapping in local states. Furthermore, the proposed IGWO does not require more adjustable parameters that can enhance the convergence to the best global solution rapidly. The new IGWO is compared with recent optimization algorithms such as the mayfly optimization algorithm (MA), chimp optimization algorithm (COA), dynamic arithmetic optimization algorithm (DAOA), whale optimization algorithm (WOA), jaya algorithm (JA), archimedes optimization algorithm (AOA), and equilibrium optimizer (EO) as well as the original GWO. Besides, the performance of the designed AMPC is compared with the fuzzy logic proportional integral (FLPI) controller and the traditional MPC. Various test scenarios are performed to assert the robustness and effectiveness of the designed AMPC against the variation of the velocity, road curvature, time delay, and look ahead distance. The results emphasize that the proposed AMPC can perform the best damping response with an overshoot of around 1.3 m and a settling time of around 1.12 s less than the FLPI controller and the traditional MPC.

Understanding Physician's Perspectives on AI in Health Care: Protocol for a Sequential Multiple Assignment Randomized Vignette Study.

As the availability and performance of artificial intelligence (AI)-based clinical decision support (CDS) systems improve, physicians and other care providers poised to be on the front lines will be increasingly tasked with using these tools in patient care and incorporating their outputs into clinical decision-making processes. Vignette studies provide a means to explore emerging hypotheses regarding how context-specific factors, such as clinical risk, the amount of information provided about the AI, and the AI result, may impact physician acceptance and use of AI-based CDS tools. To best anticipate how such factors influence the decision-making of frontline physicians in clinical scenarios involving AI decision-support tools, hypothesis-driven research is needed that enables scenario testing before the implementation and deployment of these tools. This study's objectives are to (1) design an original, web-based vignette-based survey that features hypothetical scenarios based on emerging or real-world applications of AI-based CDS systems that will vary systematically by features related to clinical risk, the amount of information provided about the AI, and the AI result; and (2) test and determine causal effects of specific factors on the judgments and perceptions salient to physicians' clinical decision-making. US-based physicians with specialties in family or internal medicine will be recruited through email and mail (target n=420). Through a web-based survey, participants will be randomized to a 3-part "sequential multiple assignment randomization trial (SMART) vignette" detailing a hypothetical clinical scenario involving an AI decision support tool. The SMART vignette design is similar to the SMART design but adapted to a survey design. Each respondent will be randomly assigned to 1 of the possible vignette variations of the factors we are testing at each stage, which include the level of clinical risk, the amount of information provided about the AI, and the certainty of the AI output. Respondents will be given questions regarding their hypothetical decision-making in response to the hypothetical scenarios. The study is currently in progress and data collection is anticipated to be completed in 2024. The web-based vignette study will provide information on how contextual factors such as clinical risk, the amount of information provided about an AI tool, and the AI result influence physicians' reactions to hypothetical scenarios that are based on emerging applications of AI in frontline health care settings. Our newly proposed "SMART vignette" design offers several benefits not afforded by the extensively used traditional vignette design, due to the 2 aforementioned features. These advantages are (1) increased validity of analyses targeted at understanding the impact of a factor on the decision outcome, given previous outcomes and other contextual factors; and (2) balanced sample sizes across groups. This study will generate a better understanding of physician decision-making within this context. DERR1-10.2196/54787.

A universal fault diagnosis framework for marine machinery based on domain adaptation

In the digital and automated era, the significance of machine health monitoring has been amplified, especially for marine machinery, which plays a pivotal role in the sustainable advancement of modern industries. Two salient challenges currently hampering fault diagnostics are spotlighted: firstly, the variability in mechanical operating conditions gives rise to inconsistent data distributions, thereby prompting domain shift. Secondly, unobserved in training datasets, testing scenarios may unveil unpredictable, unknown fault modes, culminating in class discrepancies. To effectively tackle these intertwined challenges, we introduce the Two-Stage Universal Domain Adaptation (TSUDA) — a novel fault diagnosis methodology. TSUDA adeptly differentiates new fault types from labeled known counterparts by leveraging a composite transferability metric. Furthermore, it deploys a parameter-adaptive unsupervised algorithm to discern the count of emerging fault types. Rigorous experiments on datasets from marine diesel engines, emblematic of marine machinery, demonstrated TSUDA's superiority over conventional domain adaptation techniques in diverse diagnostic challenges, registering accuracy rates surpassing 90%. While the effectiveness of TSUDA is authenticated within marine machinery contexts in this investigation, its architecture suggests potential applicability in a broader spectrum of mechanical fault diagnostic scenarios.

Collaborative Scheduling Optimization of Container Port Berths and Cranes under Low-Carbon Environment

Motivated by the need for a green and low-carbon economy, we explore the co-scheduling optimization of berths and cranes. Our aim is to balance the carbon tax and operating costs of ports under uncertain conditions, proposing an innovative nonlinear mixed-integer programming formulation. To address this optimization challenge, we have developed an enhanced version of the adaptive spiral flying dung beetle algorithm (ASFDBO). In order to evaluate the performance of the ASFDBO algorithm, we performed a benchmark function test and a convergence analysis with other recognized metaheuristics. In addition, we verified the practical applicability of the ASFDBO algorithm in different test scenarios. Through numerical experiments, we analyze the feasibility and effectiveness of the algorithm’s scheduling solutions and improvement strategies. Results indicate that our collaborative scheduling optimization, which considers both carbon and production costs, achieves feasible solutions and reduces carbon expenses. Finally, we investigate the impact of different carbon tax rates on the joint scheduling optimization of berths and quay cranes, and the results show that a reasonable carbon tax policy can effectively reduce the carbon emissions of ports.

Softening the Blow: The Power of Rhetorical Frames Used by Social Media Influencers with Sponsorship Disclosure

This study explores how social media influencers’ (SMIs) endorsements preceding commercials can increase positive response, even when sponsorship is disclosed. We employed three rhetorical frames—transparency, co-orientation, and downplaying—in a real-world test scenario involving TV commercials for two energy corporations with problematic reputations. A national survey with 644 respondents revealed that each rhetorical frame significantly bolstered positive attitudes toward the featured corporations. These attitude shifts were mediated by reductions in persuasion knowledge and increases in message credibility. Our findings not only support but also extend existing theories on endorsement effectiveness by integrating the insights from rhetorical theory. It shows that understanding the nuances of ‘how’ a message is delivered, through rhetorical framing, can be as crucial as ‘what’ is being said.

Novel item selection strategies for cognitive diagnostic computerized adaptive testing: A heuristic search framework.

The computerized adaptive form of cognitive diagnostic testing, CD-CAT, has gained increasing attention in the domain of personalized measurements for its ability to categorize individual mastery status of fine-grained attributes more accurately and efficiently through administering items tailored to one's ability progressively. How to select the next item based on previous response(s) is crucial for the success of CD-CAT. Previous item selection strategies for CD-CAT have often followed a greedy or semi-greedy approach, which makes it difficult to strike a balance between diagnostic performance and item bank utilization. To address this issue, this study takes a graph perspective and transforms the item selection problem in CD-CAT into a path-searching problem, in which paths refer to possible test construction and nodes refer to individual items. A heuristic function is defined to predict the prospect of a path, indicating how well the corresponding test can diagnose the current examinee. Two search mechanisms with different biases towards item exposure control are proposed to approximate the optimal path with the best prospect. The first unused item on the resulting path is selected as the next item. The above components compose a novel CD-CAT item selection framework based on heuristic search. Simulation studies are conducted under a variety of conditions regarding bank designs, bank-quality conditions, and testing scenarios. The results are compared with different types of classic item selection strategies in CD-CAT, showing that the proposed framework can enhance bank utilization at a smaller cost of diagnostic performance.

Economically optimal operation of recirculating aquaculture systems under uncertainty

This study presents a novel economical operation scheme for recirculating aquaculture systems (RASs). The proposed approach is comprised of a moving horizon estimator and an economic model predictive controller (EMPC), which, respectively, provide the necessary feedback for the EMPC and make economically optimal decisions for the RAS. The scheme is enabled by a mechanistic RAS model, which is coupled with an objective function that jointly optimizes the economics of aquatic biomass growth and utility expenditure. The proposed scheme is tested on a rainbow trout case study, whereby it is compared against the previous state-of-the-art control approach in aerator failure and temperature disturbance scenarios. In both test scenarios, EMPC scheme outperforms the previous state state-of-the-art controller in terms of process economics (up to 41% improvement) as well as shorter batch times (up to 22% shortening). Furthermore, the scheme is shown to be capable of estimating uncertain parameters with little error (generally in the order of 5%) and negligible effect on economic performance when compared to cases with full measurement access and no uncertainty. The proposed scheme provides a new and automated way to ensure RASs operate with maximal productivity, thus further incentivizing sustainability and the uptake of this technology.

Ultra-fast tuning of neural network controllers with application in path tracking of autonomous vehicle

Neural network (NN) controllers have shown great potential in solving complex control or decision-making tasks. However, most of the NN controllers either rely on the availability of large datasets or require dense interactions with the environment, which hinders their application in real systems. In this paper, we introduce a model-based reinforcement learning (MBRL) algorithm, aimed at realizing ultra-fast tuning of deep NN controller from a small sample set of real-world data. The algorithm uses Gaussian processes (GPs) to model the unknown dynamics of real system and updates controller parameters through stochastic gradient descent. By using particle-based method for long-term predictions, the algorithm can easily incorporate online state estimators and filters into controller learning, which is conductive to learning from systems with partially measurable states and stochastic control delay. We apply the algorithm to calibrate a deep NN controller for the path tracking of a full-size autonomous vehicle (AV). Simulation and field test results show that the deep NN controller can be well calibrated after only one interaction with the environment and can achieve similar tracking performance to optimization-based methods such as nonlinear model prediction control (NMPC) in various test scenarios by combining with a feed-forward pure pursuit (PP) controller.

A nutritional modelling framework for inclusion in a Norwegian food system model

The aim of this study was to apply a comprehensive mathematical model designed to assess the current and future availability of food, as well as the resulting nutrient availability and intake, in the context of the Norwegian population. The model explores various scenarios, including self-sufficiency levels for food supply and impacts on nutrition recommendations. A food mass balance charting production, import, export, feed, seed, and consumer allocation in the Norwegian food system provided insights into the nutrients available to the population. The analysis included a comparison with two versions of the Nordic Nutrient Recommendations (NNR)1, with the latest version recommending substantial changes in food consumption compared to current dietary patterns in Norway. The nutrient analysis compared the food mass to Matvaretabellen2 and was supplemented with data from the United Nations World Population Prospects3 and the Food and Agriculture Organisation/World Health Organisation/United Nations4 report to ensure comprehensive nutrient analysis. Micronutrient gaps were observed in Iodine (94% of the target intake) and Vitamin D (46% of the target intake), while saturated-fatty acids slightly exceeded the recommended requirements (107% of the upper limit) based on the current baseline scenario. The updated NNR4 recommends changes to specific food categories, namely fruits, vegetables, nuts, and seeds. A secondary scenario testing compared against the updated NNR found that increasing the availability of the supply of these groups does not result in any new nutrient gaps, demonstrating the feasibility of addressing the issue on a national supply basis. These approach offers a mathematical-modelling based tool that can be used to provide information for a national food system. Leveraging the model’s capacity to simulate various scenarios, informed decisions to optimise self-sufficiency levels and align food supply with recommended nutritional guidelines can be made. To improve the model, higher data resolution and clearer categorisation of food groups are required which can then be linked into a more complete national food system. The mathematical model presented in this study provides a framework for understanding of food and nutrient availability in Norway. By identifying critical nutrient gaps and potential solutions, this research contributes knowledge for a healthier and sustainable food future for the nation.

Analysis of Pneumonia on Chest X-Ray Images Using Convolutional Neural Network Model iResNet-RS

Pneumonia, a prevalent inflammatory condition affecting lung tissue, poses a significant health threat across all age groups and remains a leading cause of infectious mortality among children worldwide. Early diagnosis is critical in preventing severe complications and potential fatality. Chest X-rays are a valuable diagnostic tool for pneumonia; however, their interpretation can be challenging due to unclear images, overlapping diagnoses, and various abnormalities. Consequently, expedient, and accurate analysis of medical images using computer-aided methods has become crucial. This research proposes a Convolutional Neural Network (CNN) model, specifically the ResNet-RS Model, to automate pneumonia identification. The Contrast Limited Adaptive Histogram Equalization (CLAHE) technique enhances image contrast and highlights abnormalities in pneumonia images. Additionally, data augmentation techniques are applied to expand the image dataset while preserving the intrinsic characteristics of the original images. The proposed methodology is evaluated through three testing scenarios, employing chest X-ray images and pneumonia dataset. The third testing scenario, which incorporates the ResNet-RS model, CLAHE preprocessing, and data augmentation, achieves superior performance among these scenarios. The results show an accuracy of 92% and a training loss of 0.0526. Moreover, this approach effectively mitigates overfitting, a common challenge in deep learning models. By leveraging the power of the ResNet-RS model, along with CLAHE preprocessing and data augmentation techniques, this research demonstrates a promising methodology for accurately detecting pneumonia in chest X-ray images. Such advancements contribute to the early diagnosis and timely treatment of pneumonia, ultimately improving patient outcomes and reducing mortality rates.

ANALISIS SIMULASI PROTOKOL ROUTING DINAMIS OSPF UNTUK PENINGKATAN EFISIENSI JARINGAN HYBRID KAMPUS UNUD DENPASAR

UNUD GDLN building located in UNUD Sudirman campus acts as an information and communication technology service center as an internet access service provider, radio broadcast and online learning. The UNUD Campus network currently uses a static hybrid routing protocol, where the static routing protocol has the disadvantage that the network administrator must always reconfigure the network if there is a topology change. The Open Shortest Path First (OSPF) dynamic routing protocol can be used as a solution to overcome the weaknesses of static routing protocols because it is more suitable for larger networks and has the advantage of automatically determining the best route. This research aims to analyze the application of OSPF routing protocol in UNUD Denpasar Campus with GNS3 simulation. This research uses Quality of Service (QoS) parameters to analyze network performance, and test scenarios by PING with large data transmission that imitates YouTube video streaming. The results showed that OSPF dynamic routing protocol has better performance and has met the TIPHON standard with very good results for packet loss, throughput, and delay parameters and good results for jitter parameters. Tests on static routing protocol simulations and OSPF routing protocols are carried out by comparing the transmission process based on the resolution on YouTube video streaming imitation, starting with 360p, 480p, 720p and 1080p resolutions. Based on the results of the study, it can be concluded that OSPF routing is more suitable and produces better parameter values when transmitting streaming video with a resolution of 480p, 720p and 1080p, while the transmission of streaming video with a resolution of 360p is more suitable for static routing which is more suitable for transmission with lower resolutions.

A randomized encryption algorithm for the MPEG-DASH digital rights management

ABSTRACT This article addresses the pervasive issue of digital piracy in the movie industry. Despite substantial efforts to combat piracy, its impact remains significant, resulting in substantial financial losses for companies. Digital rights management (DRM) has been introduced as a countermeasure to counter this challenge. While the security of video applications, including commercial and military videos, has gained prominence in recent research, this paper focuses on enhancing the encryption algorithm and presenting encryption results. Specifically, it explores the AES 128 encryption algorithm’s vulnerabilities in the MPEG-DASH media file and proposes a randomized algorithm for video file encryption. To bolster security, the randomized approach leverages three robust algorithms: AES 256, Two-Fish, and Serpent. The randomized algorithm demonstrated significantly higher resistance to brute-force attacks than AES 128 CBC, as exemplified by a test scenario with a key length of 32 and 100 media chunks. By utilizing this combination of unbroken algorithms, the paper offers an efficient solution for offline-protected media storage, ultimately strengthening the security and privacy of the MPEG-DASH DRM system and mitigating the risk of media file piracy. The randomized algorithm consistently outperformed AES 128 CBC in terms of execution time across multiple media files, with the randomized algorithm completing the encryption process in 34,610 milliseconds for Media File 1, compared to AES 128 CBC, which required 59,910 milliseconds, showcasing a notable enhancement in speed.