Undefined Objects Research Articles

Improving Distributed Renewable Energy Power Planning Through Particle Swarm Algorithm

The ongoing energy structure reform in our country has led to the emergence of distributed renewable energy as a primary source of energy development and utilization, primarily due to its utilization of local resources. However, challenges such as undefined objectives and ineffective planning have impeded its progress. This study specifically investigates distributed renewable energy power planning by enhancing a particle swarm algorithm with a strategy for updating local optimal solutions. The refined algorithm tackles issues related to renewable energy variability and economic efficiency, thereby optimizing the planning of distributed renewable energy power systems. The outcomes illustrate improvements in system operation, economic viability, and environmental sustainability. This research contributes to the progression of particle swarm algorithms for the planning of distributed renewable energy power systems.

EEG-controlled tele-grasping for undefined objects.

This paper presents a teleoperation system of robot grasping for undefined objects based on a real-time EEG (Electroencephalography) measurement and shared autonomy. When grasping an undefined object in an unstructured environment, real-time human decision is necessary since fully autonomous grasping may not handle uncertain situations. The proposed system allows involvement of a wide range of human decisions throughout the entire grasping procedure, including 3D movement of the gripper, selecting proper grasping posture, and adjusting the amount of grip force. These multiple decision-making procedures of the human operator have been implemented with six flickering blocks for steady-state visually evoked potentials (SSVEP) by dividing the grasping task into predefined substeps. Each substep consists of approaching the object, selecting posture and grip force, grasping, transporting to the desired position, and releasing. The graphical user interface (GUI) displays the current substep and simple symbols beside each flickering block for quick understanding. The tele-grasping of various objects by using real-time human decisions of selecting among four possible postures and three levels of grip force has been demonstrated. This system can be adapted to other sequential EEG-controlled teleoperation tasks that require complex human decisions.

Mathematical Concept Exploration Using Turiyam Cognition

Recent time Turiyam set is considered as one of the prominent tool to explore unknown or undefined objects. The elementary school is one of the suitable examples where child explore each of the unknown objects. In this stage the child develop its Turiyam cognition based on its consciousness. It can be characterized as true learning (t), false learning, (f), uncertain learning (i) and last liberal or unknown or not required at this stage which can be explored later (l). The child refused to learn any topic can be found via 4-(t+i+f+l) in case of four independent events otherwise 1-(t+i+f+l) in case of dependent events. These unknown or liberal examples which include child quantum cognition need Turiyam logic. This paper tried to explore those concepts and examples in this paper.

Information Field and Physical Noise Generator Entanglement

In experiments with physical noise generators based on electronic or photonic quantum events, significant deviations from random distributions have been repeatedly observed. To explain these effects, intention-based interactions between consciousness and mind with physical random processes have been suggested, either being caused by individual minds or by a proposed global mind. As these explanations involve physically undefined objects such as “mind” and “consciousness”, an explanatory model based on the concept of an information field is given herein, based on the concept of generalized quantum entanglement, including the entanglement of physical noise processes with information fields and an analogy to quantum teleportation. In addition, the non-random hypothesis of using such a physical noise generator in capturing qualitative characteristics of individuals is tested in a randomized controlled study with 100 participants.

Low-Altitude Aerial Video Surveillance via One-Class SVM Anomaly Detection from Textural Features in UAV Images

In recent years, small-scale Unmanned Aerial Vehicles (UAVs) have been used in many video surveillance applications, such as vehicle tracking, border control, dangerous object detection, and many others. Anomaly detection can represent a prerequisite of many of these applications thanks to its ability to identify areas and/or objects of interest without knowing them a priori. In this paper, a One-Class Support Vector Machine (OC-SVM) anomaly detector based on customized Haralick textural features for aerial video surveillance at low-altitude is presented. The use of a One-Class SVM, which is notoriously a lightweight and fast classifier, enables the implementation of real-time systems even when these are embedded in low-computational small-scale UAVs. At the same time, the use of textural features allows a vision-based system to detect micro and macro structures of an analyzed surface, thus allowing the identification of small and large anomalies, respectively. The latter aspect plays a key role in aerial video surveillance at low-altitude, i.e., 6 to 15 m, where the detection of common items, e.g., cars, is as important as the detection of little and undefined objects, e.g., Improvised Explosive Devices (IEDs). Experiments obtained on the UAV Mosaicking and Change Detection (UMCD) dataset show the effectiveness of the proposed system in terms of accuracy, precision, recall, and F1-score, where the model achieves a 100% precision, i.e., never misses an anomaly, but at the expense of a reasonable trade-off in its recall, which still manages to reach up to a 71.23% score. Moreover, when compared to classical Haralick textural features, the model obtains significantly higher performances, i.e., ≈20% on all metrics, further demonstrating the approach effectiveness.

Detection of solder paste defects with an optimization‐based deep learning model using image processing techniques

Purpose In the production processes of electronic devices, production activities are interrupted due to the problems caused by soldering defects during the assembly of surface-mounted elements on printed circuit boards (PCBs), and this leads to an increase in production costs. In solder paste applications, defects that may occur in electronic cards are usually noticed at the last stage of the production process. This situation reduces the efficiency of production and causes delays in the delivery schedule of critical systems. This study aims to overcome these problems, optimization based deep learning model has been proposed by using 2D signal processing methods. Design/methodology/approach An optimization-based deep learning model is proposed by using image-processing techniques to detect solder paste defects on PCBs with high performance at an early stage. Convolutional neural network, one of the deep learning methods, is trained using the data set obtained for this study, and pad regions on PCB are classified. Findings A total of six types of classes used in the study consist of uncorrectable soldering, missing soldering, excess soldering, short circuit, undefined object and correct soldering, which are frequently used in the literature. The validity of the model has been tested on the data set consisting of 648 test data. Originality/value The effect of image processing and optimization methods on model performance is examined. With the help of the proposed model, defective solder paste areas on PCBs are detected, and these regions are visualized by taking them into a frame.

Methods for adaptive control of objects with variable parameters

The work uses the method of standard characteristic polynomials, based on the Lyapunov theorem on adaptive control systems, the theory of flexible and robust control, methods of the theory of nonlinear systems. When modeling an internal combustion engine, methods of identification theory were additionally involved. When obtaining theoretical results, the method of Lyapunov functions, the method of standard characteristic polynomials, methods of the theory of adaptive and robust control, methods of the theory of nonlinear systems were used. When constructing a model of an internal combustion engine, methods of identification theory were additionally involved. For the synthesis of control systems in conditions of uncertainty, one of the topical directions is adaptive systems. These are control systems that compensate for parametric, signal, functional, or structural uncertainties of the control object by automatically adjusting the controller during the system's working operation, i.e., adaptive systems make up for the lack of a priori information about the control object during operational operation. To solve the problem of managing undefined objects, for example, classical methods are used. In such methods, when state variables are immeasurable, it becomes necessary to use additional dynamic filters. Classical methods are more often used for a limited class of objects. In the case of class extension, the structure of the control algorithm becomes more complicated.

Ω-soft sets and medical decision-making application

Pythagorean Fuzzy Set is a generalization of the Intuitionistic Fuzzy Set. Therefore, in real-life problems for uncertainty, the decision-making mechanism in Pythagorean Fuzzy Sets outcomes better than Intuitionistic Fuzzy Set decision-making. For undefined objects, Soft Set is a new approach. Pythagorean Fuzzy Soft Set is deriving with the combination of Pythagorean Fuzzy Set and Soft Set. In this work, we will define the Pythagorean Fuzzy Parametrized Pythagorean Fuzzy Soft Set(Ω-Soft Set) and investigate some properties of the new set. The domain of the parameter function given in the new definition is a complete lattice. Selecting the domain of the Ω as a complete lattice allows us to solve some problems in the definitions and operations of the subset, union, and intersection. Besides, for Ω-Soft Set and other related concepts, we will present the decision-making problem solution as an application.

Chinese patient with neurofibromatosis-Noonan syndrome caused by novel heterozygous NF1 exons 1\u201358 deletion: a case report

BackgroundNeurofibromatosis-Noonan syndrome (NFNS) is a rare autosomal dominant hereditary disease. We present a case of NFNS due to the heterozygous deletion of exons 1–58 of the NF1 gene on chromosome 17 in a 15-month-old boy.Case presentationA 15-month-old boy was admitted for motor and language developmental delay, numerous café-au-lait spots, hypertelorism, left blepharoptosis, pectus excavatum, cryptorchidism, secondary atrial septal defect, and UBOs (undefined bright objects) revealed by cranial MRI T2FLAIR in basal ganglia and cerebellum. Using whole exome sequencing, we identified a de novo heterozygous deletion including exons 1–58 of the NF1 gene.ConclusionAlthough genetic tests are useful tools for diagnosis of NFNS, NF1, or NS, comprehensive analysis of genetic factors and phenotypes is indispensable in the clinical practice. To the best of our knowledge, this case presents the first Chinese NFNS case due to NF1 defects, and the NF1 exons 1–58 deletion-related phenotype is unlike any other reported case.

Control of Dynamic Objects in the Conditions of Uncertainty in the Point Sliding Mode

There is development of the well-known sliding mode, which in the classical formulation didn’t find the development to be applied to control systems discussed. Alternatively, there is method of organizing one of the uniformity of the sliding mode called the "point sliding mode" proposed. The distinctive feature of this mode is that here the control gaps occur at time-equal points of the switching line (hyperplane) which allows the origin of coordinates for a finite number of switches. The possibility of changing the time interval between these points makes it possible to obtain various modes: a finite mode, in which a given point is reached from any initial state in one switch, and in this mode the switch line is "isochronous"; point sliding mode in which a given point is reached in a finite number of switchings; limit mode, when the length of time intervals tend to zero, and the switching frequency to infinity. Considering this feature the concept of "degree of slip" is introduced. It is shown that in the case of forced movement in the SPS, a sliding motion is observed, which does not allow for ensuring invariance with respect to external disturbances. There are two ways to eliminate the forced component of the movement offered. One of the advantages of using a point sliding mode is that, in order to improve performance, it is not necessary to use a boundary layer, which is realized by entering various logical conditions into the control algorithm. The practical significance of a point sliding mode lies in the fact that, with a small switching frequency, it is possible to maintain the quality indices of an undefined object within an acceptable interval. The studies were conducted for onedimensional second-order linear systems (SISO). Results can be generalized for higher order multidimensional systems. Solution of model problems on MATLAB / Simulink allows us to make a number of positive conclusions that are of great practical importance in terms of expanding the area of use of skipping modes, especially in relation to the management of undefined objects.

Renormalization of SU(2) Yang–Mills theory with flow equations

We give a proof of perturbative renormalizability of SU(2) Yang–Mills theory in four-dimensional Euclidean space which is based on the flow equations of the renormalization group. The main motivation is to present a proof which does not make appear mathematically undefined objects (as, for example, dimensionally regularized generating functionals), which permits to parametrize the theory in terms of physical renormalization conditions, and which allows to control the singularities of the correlation functions of the theory in the infrared domain. Thus a large part of the proof is dedicated to bounds on massless correlation functions.

Generalized rough sets based on neighborhood systems and topological spaces

Abstract Rough sets theory is an important method for dealing with uncertainty, fuzziness and undefined objects. In this paper, we introduce a new approach for generalized rough sets based on the neighborhood systems induced by an arbitrary binary relation. Four pairs of the dual approximation operators are generated from the core of neighborhood systems. Relationship among different approximation operators are presented. We generate different topological spaces by using the core of these neighborhood systems. Relationship among different generated topologies are discussed.

Aprendizagem de discriminação auditivo-visual a partir de uma única tentativa de exclusão com objetos tridimensionais

O responder por exclusão é um padrão consistente de comportamento descrito como a tendência em selecionar o objeto indefinido, entre outros definidos, diante de um nome indefinido. Como a relação nome-objeto nem sempre se mantém após uma tentativa, o objetivo deste estudo foi verificar o efeito da apresentação de características do objeto indefinido na aprendizagem. Foi solicitado que os participantes (10 crianças de 24 a 34 meses) selecionassem brinquedos correspondentes às palavras ditadas, dentre um conjunto (linha de base com objetos e palavras definidos); então, eram conduzidas sondas de exclusão e de verificação de aprendizagem. Características específicas dos objetos indefinidos eram demonstradas antes das sondas na Condição Com Demonstração (CD) e não ocorriam na Condição Sem Demonstração (SD). Oito crianças responderam por exclusão em todas as sondas; não houve diferenças significativas entre as condições na aprendizagem. Os dados sugerem que a demonstração de características do objeto não contribui para a aprendizagem.

Detection and Classification of Changes in Buildings from Airborne Laser Scanning Data

The difficulty associated with the Lidar data change detection method is lack of data, which is mainly caused by occlusion or pulse absorption by the surface material, e.g., water. To address this challenge, we present a new strategy for detecting buildings that are “changed”, “unchanged”, or “unknown”, and quantifying the changes. The designation “unknown” is applied to locations where, due to lack of data in at least one of the epochs, it is not possible to reliably detect changes in the structure. The process starts with classified data sets in which buildings are extracted. Next, a point-to-plane surface difference map is generated by merging and comparing the two data sets. Context rules are applied to the difference map to distinguish between “changed”, “unchanged”, and “unknown”. Rules are defined to solve problems caused by the lack of data. Further, points labelled as “changed” are re-classified into changes to roofs, walls, dormers, cars, constructions above the roof line, and undefined objects. Next, all the classified changes are organized as changed building objects, and the geometric indices are calculated from their 3D minimum bounding boxes. Performance analysis showed that 80%–90% of real changes are found, of which approximately 50% are considered relevant.

New ASBOs for old?

The Anti-Social Behaviour Order (ASBO) was designed as a civil/criminal hybrid, preventive in structure and with a largely undefined object. After 2002, legal challenges to the ASBO led to the use of justificatory arguments from cumulative effect, and to the introduction of new measures which offered to regulate anti-social behaviour in more legally acceptable forms. In 2014 the Coalition government replaced the ASBO with two new instruments: a post-conviction Criminal Behaviour Order (CBO) and a wholly-civil anti-social behaviour injunction (ASB Injunction). While the CBO and the ASB Injunction build on this history, it is argued that they do not represent a new approach to anti-social behaviour so much as a continuation of the ASBO by other means.

Designing a performance measurement system to support outsourcing decisions in a Finnish university

In recent years, the role and importance of performance measurement in public organisations has been much emphasised. However, the discussion on the issue so far has been on a rather general level. This paper aims to understand the challenges faced by public organisations in developing performance measurement systems (PMSs) for a specific managerial purpose, to support outsourcing decisions. The objective of the paper is approached with a literature review and a single qualitative case study carried out as an action research. The case study introduces a detailed description of a PMS development project carried out in the support services of a Finnish university. A framework developed in prior research is utilised to analyse the impacts of two typical characteristics (conflicting stakeholder needs and undefined objectives) of public organisations in a PMS design project. The contribution of the paper is to extend the existing understanding about public sector PMS especially regarding two key points: 1) to understand the impact of different purposes for measurement (in this case the outsourcing context) on the PMS development process; 2) to understand the need to align the PMS system with the organisational culture and other managerial control systems in order to achieve desired performance outcomes.

My new colleague has artificial muscles: a DEA based approach for inherently compliant robotic systems

When robots and human workers team up, safety should be ensured at all times. In order to improve safety of human–robot collaboration in hybrid manufacturing processes, many different types of compliant robotic drives, serial elastic actuators for instance, have been developed to date. However, most of them still consist of rigid mechanical components in combination with prevailing servo motor and thus bring several disadvantages such as poor power-to-weight ratio. This paper presents our solution approach for realization of biomimetic, inherently compliant artificial muscles based on dielectric elastomer actuators (DEAs). The artificial muscles based on DEAs distinguish themselves from conventional actuators through their favorable characteristics. They (a) work noiseless, (b) feature specific energy density comparable to human skeletal muscles and (c) are capable of storing or even recovering energy, and finally, (d) can adjust their geometry to meet with undefined and unstructured objects or environments. With remaining challenges overcome, the DEAs are expected to provide a significant contribution to safety of industrial robots collaborating with human workers.

Multiple-entity based classification of airborne laser scanning data in urban areas

There are two main challenges when it comes to classifying airborne laser scanning (ALS) data. The first challenge is to find suitable attributes to distinguish classes of interest. The second is to define proper entities to calculate the attributes. In most cases, efforts are made to find suitable attributes and less attention is paid to defining an entity. It is our hypothesis that, with the same defined attributes and classifier, accuracy will improve if multiple entities are used for classification. To verify this hypothesis, we propose a multiple-entity based classification method to classify seven classes: ground, water, vegetation, roof, wall, roof element, and undefined object. We also compared the performance of the multiple-entity based method to the single-entity based method.Features have been extracted, in most previous work, from a single entity in ALS data; either from a point or from grouped points. In our method, we extract features from three different entities: points, planar segments, and segments derived by mean shift. Features extracted from these entities are inputted into a four-step classification strategy. After ALS data are filtered into ground and non-ground points. Features generalised from planar segments are used to classify points into the following: water, ground, roof, vegetation, and undefined objects. This is followed by point-wise identification of the walls and roof elements using the contextual information of a building. During the contextual reasoning, the portion of the vegetation extending above the roofs is classified as a roof element. This portion of points is eventually re-segmented by the mean shift method and then reclassified.Five supervised classifiers are applied to classify the features extracted from planar segments and mean shift segments. The experiments demonstrate that a multiple-entity strategy achieves slightly higher overall accuracy and achieves much higher accuracy for vegetation, in comparison to the single-entity strategy (using only point features and planar segment features). Although the multiple-entity method obtains nearly the same overall accuracy as the planar-segment method, the accuracy of vegetation improves by 3.3% with the rule-based classifier. The multiple-entity method obtains much higher overall accuracy and higher accuracy in vegetation in comparison to using only the point-wise classification method for all five classifiers.Meanwhile, we compared the performances of five classifiers. The rule-based method provides the highest overall accuracy at 97.0%. The rule-based method provides over 99.0% accuracy for the ground and roof classes, and a minimum accuracy of 90.0% for the water, vegetation, wall and undefined object classes. Notably, the accuracy of the roof element class is only 70% with the rule-based method, or even lower with other classifiers. Most roof elements have been assigned to the roof class, as shown in the confusion matrix. These erroneous assignments are not fatal errors because both a roof and a roof element are part of a building. In addition, a new feature which indicates the average point space within the planar segment is generalised to distinguish vegetation from other classes. Its performance is compared to the percentage of points with multiple pulse count in planar segments. Using the feature computed with only average point space, the detection rate of vegetation in a rule-based classifier is 85.5%, which is 6% lower than that with pulse count information.

Detection and Classification of Changes in Buildings from Airborne Laser Scanning Data

Abstract. Building change detection serves to investigate illegal buildings. Illegal built or removed structures, especially those concealed among gable roofs such as dormers, are difficult to track among potentially millions of buildings. Nevertheless, they can be efficiently located in changed areas. An approach is proposed to automatically detect and classify changes in buildings from two epochs of Airborne Laser Scanning Data. Both datasets are classified into water, ground, building, vegetation and undefined objects in advance. After generalization of a 3D surface separation map, we verify changes by making rules on the separation map. Changes belonging to buildings are then classified into roof, wall, dormers, vehicles, construction above roof and undefined objects. As the ALS data has accuracy in strip difference of lower than 5 cm within the same epoch and from different epochs, changes that are larger than 10 cm were detected. Building changes, which areas are larger than 4 m2, are identified as change. By inspection, nearly all changes are detected and approximately 80% changes are correctly classified.

밀리미터파 복사계의 제작

Abstract We have manufactured a close range Dicke type radiometer which consists of two stage low noise amplifier and diode detector. Frequency range of this system is 35 GHz. And this is used for studying temperature calibration on specific objects. We have present millimeter-wave radiometer's thermal calibration method and its characteristics. From absolute temperature 299K to 309K, in proportion to increase temperature, output voltages are linearly increased. In this case, undefined objects can be measured thermal noise temperature relatively. Overall from absolute temperature 214K to 309K, we have obtained relation of temperature and output voltage;V= 0.03601K - 10.70517. Key Words : Dicke type radiometer, Millimeter-wave radiometer, Thermal noise, Calibration * 정회원, 상지대학교 이공대학 응용물리전자학과접수일자 2010.9.4, 수정완료 2012.5.15게재확정일자 2012.6.8.Received: 4 September, 2010, Revised: 15 May, 2012Accepted: 8 June, 2012 * Corresponding Author:kookim@dongguk.eduDept. of Applied Physics & Electronics, Sangji University, Korea