International Journal of Intelligent Systems and Applications @ijisa
Статьи журнала - International Journal of Intelligent Systems and Applications
Все статьи: 1203
Статья научная
Combined with the transmission ratio charac-teristics of forklift steering-by-wire (SBW) system, through the application of fuzzy control technology, the variable transmission ratio function is designed based on the steering handle angle and vehicle speed, and simula-tion analysis of sinusoidal steering is done at low-speed and high-speed. Simulation results show that the fuzzy variable transmission ratio control can make forklift steering light & sensitive at low-speed and steering steady & heavy at high speed, also it can improve the operation stability and reduce the driver's load. Discuss the relationship between yaw rate and forklift handling stability, propose the yaw rate feedback control strategy based on the fuzzy variable transmission ratio control, and design a fuzzy self-adaptive PID controller. Simula-tion results show that the SBW system based on the fuzzy variable transmission ratio control with yaw rate feedback can accurately and quickly track the desired yaw rate, and reduce or even eliminate the overshoot phenomenon, improve the forklift dynamic performance.
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Research on temperature field and temperature stress of prestressed concrete girders
Статья научная
This paper introduces the establishment and simplification of the temperature field and the general calculation method of temperature stress of the prestressed concrete box girders. Three kinds of sunshine temperature gradient models were loaded to a real bridge respectively, and got stress and displacement curves. Research data of several prestressed concrete box girders were selected from different regions of China to compare the relative error of the calculated and measured value. We indicate that the study of temperature field and thermal stress of prestressed concrete box girders is necessary, and will help engineers to solve the problem in structure design.
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Research on the Layout of National Economic Mobilization Logistics Centers
Статья научная
The problem of the layout of NEMLC (National Economic Mobilization Logistics Center) is one of the most important long-term decision-making issues. The result of NEMLC’s layout directly impacts many aspects of mobilization, such as time, reliability, quality, efficiency, cost, and so on, consequently affects the effect of the mobilization. Reasonable NEMLC layout can bring people convenience in the daily life, reduce costs, and improve service efficiency and competitiveness. Poor NEMLC layout often brings a great deal of inconvenience and loss, and even leads to mobilization failure. Under the restriction of mobilization time, the paper establishes the layout model that one or more mobilization logistics centers provide the material to the mobilization demanding place. The mobilization goods or service can reach the demanding place to carry into mobilization execution within the given time, and the number of the built NEMLCs is the least.
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Review and Comparison of Kernel Based Fuzzy Image Segmentation Techniques
Статья научная
This paper presents a detailed study and comparison of some Kernelized Fuzzy C-means Clustering based image segmentation algorithms Four algorithms have been used Fuzzy Clustering, Fuzzy C-Means(FCM) algorithm, Kernel Fuzzy C-Means(KFCM), Intuitionistic Kernelized Fuzzy C-Means(KIFCM), Kernelized Type-II Fuzzy C-Means(KT2FCM).The four algorithms are studied and analyzed both quantitatively and qualitatively. These algorithms are implemented on synthetic images in case of without noise along with Gaussian and salt and pepper noise for better review and comparison. Based on outputs best algorithm is suggested.
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Review, Design, Optimization and Stability Analysis of Fractional-Order PID Controller
Статья научная
This paper will establish the importance and significance of studying the fractional-order control of nonlinear dynamical systems. The foundation and the sources related to this research scope is going to be set. Then, the paper incorporates a brief overview on how this study is performed and present the organization of this study. The present work investigates the effectiveness of the physical-fractional and biological-genetic operators to develop an Optimal Form of Fractional-order PID Controller (O2Fo-PIDC). The newly developed Fo-PIDC with optimal structure and parameters can, also, improve the performances required in the modeling and control of modern manufacturing-industrial process (MIP). The synthesis methodology of the proposed O2Fo-PIDC can be viewed as a multi-level design approach. The hierarchical Multiobjective genetic algorithm (MGA), adopted in this work, can be visualized as a combination of structural and parametric genes of a controller orchestrated in a hierarchical fashion. Then, it is applied to select an optimal structure and knowledge base of the developed fractional controller to satisfy the various design specification contradictories (simplicity, accuracy, stability and robustness).
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Rice Leaf Disease Recognition using Local Threshold Based Segmentation and Deep CNN
Статья научная
Timely detection of rice diseases can help farmers to take necessary action and thus reducing the yield loss substantially. Automatic recognition of rice diseases from the rice leaf images using computer vision and machine learning can be beneficial over the manual method of disease recognition through visual inspection. During the recent years, deep learning, a very popular and efficient machine learning algorithm, has shown great promise in image classification task. In this paper, a segmentation-based method using deep neural network for classifying rice diseases from leaf images has been proposed. Disease-affected regions of the rice leaves have been segmented using local segmentation method and the Convolutional Neural Network (CNN) has been trained with those images. Proposed method has been applied on three different datasets including the one created by us which consists of the rice leaf images collected from Bangladesh Rice Research Institute (BRRI). Three state-of-the-art CNN architectures VGG, ResNet and DenseNet, used in the proposed method, have been trained with these three datasets for classifying the diseases. Classification performance of the proposed method using the said three CNN architectures for the three datasets have been analyzed and compared. These results show that this model is quite promising in classifying rice leaf diseases. Outcome of this research is an enhancement in the performance of rice disease classification which is quite significant for the viability of this work to be transformed into a real-time application for the farmers.
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Robot Based Preventive Maintenance System for In-Service Inspection of Equipments
Статья научная
The current tendency in the petrochemical plants and Aerospace industry is to save weight and life of the equipments in the processing units, in order to reduce costs and to ensure remaining life of components. The world of today is improving with respect to new technologies. As a result, new composites are added to material for gaining a new shape and structure of material. This has made it compulsory for new innovative in non destructive testing specially in ultrasonic inspection, from conventional ultrasonic based on pulse echo method to phased array methods. In parallel to this innovative in ultrasonic, mechanical manipulator have evolved for grinding and take a setup to unapproachable zones. Industrial robot with ultrasonic inspection developed in this paper, with the collaboration of Indian Oil Corporation, Faridabad, India. Which are bringing reliability, durability, accuracy, flexibility, good maintenance and reproducibility for different sizing components to in-service inspection? This paper focused on the evolution of in-service ultrasonic probe with mechanical manipulator in non destructive testing. This evolution system has led to the current scenario systems, where a perfect combination of innovative methods in ultrasonic techniques and robots is meet to our expected delivers.
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Robust Fuzzy PD Method with Parallel Computed Fuel Ratio Estimation Applied to Automotive Engine
Статья научная
Both fuzzy logic and computed fuel ratio can compensate the steady-state error of proportional-derivative (PD) method. This paper presents parallel computed fuel ratio compensation for fuzzy plus PID control management with application to internal combustion (IC) engine. The asymptotic stability of fuzzy plus PID control methodology with first-order computed fuel ratio estimation in the parallel structure is proven. For the parallel structure, the finite time convergence with a super-twisting second-order sliding-mode is guaranteed.
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Robust Price Tag Recognition Using Optimized Detection Pipelines
Статья научная
This study investigates the enhancement of the YOLOv5 model for price tag detection in retail environments, aiming to improve both accuracy and robustness. The research utilizes the "Price Tag Detection" dataset from SOVAR, which contains 1,073 annotated images covering four classes: price tags, labels, prices, and products and is split into training, validation, and test sets with extensive preprocessing and augmentation such as resizing, rotation, color adjustments, blur, noise, and bounding box transformations. Several modifications to the YOLOv5 architecture were proposed, including advanced image augmentation techniques to simulate real-world variations in lighting and noise, enhanced anchor box optimization through K-means clustering on the dataset annotations to better fit typical price tag shapes, and the integration of the Convolutional Block Attention Module (CBAM) to enable the model to selectively focus on relevant spatial and channel-wise features. The combined application of these enhancements resulted in a substantial improvement, with the model achieving a mean Average Precision (mAP) of 96.8% at IoU 0.5 compared to the baseline YOLOv5's 92.5%. The attention mechanism and optimized anchor boxes notably improved detection of small, partially occluded, and diverse price tags, highlighting the effectiveness of combining data-driven augmentation, architectural tuning, and attention mechanisms to address the challenges posed by cluttered and dynamic retail scenes.
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Robust, Interactive, and Intelligent Captcha Model Based on Image Processing
Статья научная
Ensuring online security against automated attacks remains a critical challenge, as traditional CAPTCHA mechanisms often struggle to balance robustness and usability. This study proposes a novel intelligent and interactive CAPTCHA system that integrates advanced image processing techniques with a convolutional neural network (CNN)-based evaluation model to enhance security and user engagement. The proposed CAPTCHA dynamically generates images with randomized object placement, adaptive noise layers, and geometric transformations, making them resistant to AI-based solvers. Unlike conventional CAPTCHAs, this approach requires users to interact with images by selecting and marking specific objects, creating a human-in-the-loop validation process. For evaluation, a CNN-based classifier processes user selections and determines their validity. A lightweight embedded software module tracks user interactions in real-time, monitoring selection accuracy and response patterns to improve decision-making. The system was tested on 6,000 images across five categories (airplanes, cars, cats, motorcycles, and fish), with an 80% training and 20% testing split. Experimental results demonstrate a classification accuracy of 99.58%, validation accuracy of 96.15%, and a loss value of 0.2078. The CAPTCHA evaluation time was measured at 47–53 milliseconds for initial validation and 17–23 milliseconds for subsequent evaluations. These results confirm that the proposed CAPTCHA model effectively differentiates human users from bots while maintaining usability, demonstrating superior resilience against automated solvers compared to traditional approaches.
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Role of Explainable AI in Crop Recommendation Technique of Smart Farming
Статья научная
Smart farming is undergoing a transformation with the integration of machine learning (ML) and artificial intelligence (AI) to improve crop recommendations. Despite the advancements, a critical gap exists in opaque ML models that need to explain their predictions, leading to a trust deficit among farmers. This research addresses the gap by implementing explainable AI (XAI) techniques, specifically focusing on the crop recommendation technique in smart farming. An experiment was conducted using a Crop recommendation dataset, applying XAI algorithms such as Local Interpretable Model-agnostic Explanations (LIME), Differentiable InterCounterfactual Explanations (dice_ml), and SHapley Additive exPlanations (SHAP). These algorithms were used to generate local and counterfactual explanations, enhancing model transparency in compliance with the General Data Protection Regulation (GDPR), which mandates the right to explanation. The results demonstrated the effectiveness of XAI in making ML models more interpretable and trustworthy. For instance, local explanations from LIME provided insights into individual predictions, while counterfactual scenarios from dice_ml offered alternative crop cultivation suggestions. Feature importance from SHAP gave a global perspective on the factors influencing the model's decisions. The study's statistical analysis revealed that the integration of XAI increased the farmers' understanding of the AI system's recommendations, potentially reducing food insufficiency by enabling the cultivation of alternative crops on the same land.
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Rope Climbing Robot with Surveillance Capability
Статья научная
In the past different engineers and researcher developed robots capable of climbing for various purposes. In this paper we have developed a robot capable of rope climbing in both horizontal and vertical direction. Furthermore, the robot has the ability to perform surveillance using a camera mounted on top of the robot. The quality of the transmitted video from the camera to the computer is clear and stable. Hence the developed robot is a good choice for surveillance purposes. In addition, it can be used to traverse floors of a building. It uses an IR sensor to sense strips attached at each floor. Once the strips are sensed, a dropping mechanism is activated in which a specific object is dropped to the targeted floor or location. The robot can work in automatic mode or manual through RF signals from an RF transmitter. Finally the robot is cost effective compared to many other developed robots for rope climbing.
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Rough Fuzzy Relation on Two Universal Sets
Статья научная
Fuzzy set theory was introduced by L.A. Zadeh in 1965. Immediately, it has many applications in practice and in building databases, one of which is the construction of a fuzzy relational database based on similar relationship. The study of cases of fuzzy relations in different environments will help us understand its applications. In this paper, the rough fuzzy relation on Cartesian product of two universe sets is defined, and then the algebraic properties of them, such as the max, min, and composition of two rough fuzzy relations are examined. Finally, reflexive, α-reflexive, symmetric and transitive rough fuzzy relations on two universe sets are also defined.
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Rough Set Model for Nutrition Management in Site Specific Rice Growing Areas
Статья научная
The optimized fertilizer usage for better yield of rice cultivation is influenced by key factors like soil fertility, crop variety, duration, season, nutrient content of the fertilizer, time of application etc., It is observed that 60 percent of yield gap in tamilnadu is due to farmers lack of knowledge on key factors and informal sources of information by pesticide dealers. In this study the major contributing factors for fertilizer requirement and optimum crop yield were analyzed based on rough set theory. In data analytics perspective the nutrient plan is sort of multiple attribute decision-making processes. To reduce the complexity of decision making, key factors that are indiscernible to conclusion are eliminated. Our rough set based approach improved the quality of agricultural data through removal of missing and redundant attributes. After pretreatment the data formed as target information, then attribute reduction algorithm was used to derive rules. The generated rules were used to structure the nutrition management decision-making. The precision was above 88% and experiments proved the feasibility of the developed decision support system for nutrient management.
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Rule Based Ensembles Using Pair Wise Neural Network Classifiers
Статья научная
In value estimation, the inexperienced people's estimation average is good approximation to true value, provided that the answer of these individual are independent. Classifier ensemble is the implementation of mentioned principle in classification tasks that are investigated in two aspects. In the first aspect, feature space is divided into several local regions and each region is assigned with a highly competent classifier and in the second, the base classifiers are applied in parallel and equally experienced in some ways to achieve a group consensus. In this paper combination of two methods are used. An important consideration in classifier combination is that much better results can be achieved if diverse classifiers, rather than similar classifiers, are combined. To achieve diversity in classifiers output, the symmetric pairwise weighted feature space is used and the outputs of trained classifiers over the weighted feature space are combined to inference final result. In this paper MLP classifiers are used as the base classifiers. The Experimental results show that the applied method is promising.
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Rule-based Expert Systems for Selecting Information Systems Development Methodologies
Статья научная
Information Systems (IS) are increasingly becoming regarded as crucial to an organization's success. Information Systems Development Methodologies (ISDMs) are used by organizations to structure the information system development process. ISDMs are essential for structuring project participants’ thinking and actions; therefore ISDMs play an important role to achieve successful projects. There are different ISDMs and no methodology can claim that it can be applied to any organization. The problem facing decision makers is how to select an appropriate development methodology that may increase the probability of system success. This paper takes this issue into account when study ISDMs and provides a Rule-based Expert System as a tool for selecting appropriate ISDMs. The proposed expert system consists of three main phases to automate the process of selecting ISDMs. Three approaches were used to test the proposed expert system. Face validation through six professors and six IS professionals, predictive validation through twenty four experts and blind validation through nine employees working in IT field. The results show that the proposed system was found to be run without any errors, offered a friendly user interface and its suggestions matching user expectations with 95.8%. It also can help project managers, systems' engineers, systems' developers, consultants, and planners in the process of selecting the suitable ISDM. Finally, the results show that the proposed Rule-based Expert System can facilities the selection process especially for new users and non-specialist in Information System field.
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SMS Tracking System with Doppler Radar to Enhance Car Security for Intelligent Transport System
Статья научная
The World report on road traffic injury prevention presents some assessments and conclusions regarding road traffic accidents, in which they state that more than 1.2 million deaths per year occur on the world’s roads and around 50 million more of injured people. To prevent this people are working for intelligent transport system (ITS). ITS is trying to make an intelligent car which will be able to avoid collation. In this paper we have tried to add a new goal in ITS system which will be activated in the intelligence fails. This SMS system will help to locate a car using GPS, if the car collides. Our total development work is divided into two parts. In first part we have tried to develop a system which will help the driver by providing the road scenario using dopple radar. Doppler radar will measure the velocity of the nearby car or passing by car, depending upon the information our car will be controlled. In second part of development we have developed a auto generated SMS sending system to a specific number if the Car collide. Both systems are described in details in next part of this article.
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Safety Information Modeling: Smart Safety Devices & Internet of Everything
Статья научная
The next generation of Internet of things that connects the things, people and the process through which the people and things interact is coined as Internet of Everything. Safety management is constructed as a complex system of systems design coordinating with each other like the Fire Alarm System or Gas Detection System as well as the Emergency response like the Fire Fighters and Para-Medicals like the Ambulatory services. The governments have been setting up national broadband plans and separate dedicated spectrum for Public Safety Communications used for effective information rich emergency management and response. This paper outlines the evolution of the public safety LTE network and its applicability in the safety management system and safety preparedness. It also describes the role of Smart Objects and Internet of Everything in Safety Management. To achieve this, this paper develops the information models for safety management systems to be used in IoE utilizing the broad-band LTE networks.
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Scalar Diagnostics of the Inertial Measurement Unit
Статья научная
The scalar method of fault diagnosis systems of the inertial measurement unit (IMU) is described. All inertial navigation systems consist of such IMU. The scalar calibration method is a base of the scalar method for quality monitoring and diagnostics. Algorithms of fault diagnosis systems are developed in accordance with scalar calibration method. Algorithm verification is implemented in result of quality monitoring of IMU. A failure element determination is based in diagnostics algorithm verification and after that the reason of such failure is cleared. The process of verifications consists of comparison of the calculated estimations of biases, scale factor errors and misalignments angles of sensors to their data sheet certificate, which kept in internal memory of navigation computer. In result of such comparison the conclusion for working capacity of each one IMU sensor can be made and also the failure sensor can be deter-mined.
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Scheduling freight trains in rail-rail transshipment yards with train arrangements
Статья научная
A problem of scheduling freight trains in rail-rail transshipment yards is considered. It is solved at a deeper level compared to original papers dedicated to this problem: besides scheduling service slots for trains, this article additionally solves a problem of assigning every train to a railway track. A mathematical model and a solving method for this problem are given. A key feature of the given mathematical model is that it doesn’t use Boolean variables but rather operates with combinatorial objects (tuples of permutations). The solution method is also based on generation of combinatorial sets, which is quite an unusual approach for solving such problems.
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