Symmetric Features Research Articles

Shape Grammar as a Typology Defining Tool for Ancient Egyptian Funerary Monuments

This paper proposes a parametric shape grammar for ancient Egyptian funerary monuments. The corpus of monuments includes ten rock-cut structures, duly documented. They exhibit different grades of completion and preservation and possess variant archaeological documentation. The generation of a design following the proposed grammar depends both on formal and functional aspects. Metadata indicates the evidential value and numerical occurrence of rules in the generation of designs. The developed grammar can be used to reconstruct unfinished tombs, extend an existing one, or generate new designs according to the rules. It denotes the linear and symmetric structure characteristic of the monuments and reflects the chronological sequence of construction. The proposed grammar encodes the typology of the studied monuments and suggests that ancient Egyptian architecture may have implied a clear set of canons that may be made explicit by developing additional grammars for other building types.

Automatic Feature Recognition of Cross Holes in Hollow Cylinders

Automatic Feature Recognition (AFR) techniques provide the capabilities for translating the data of the part definition between Computer Aided Design (CAD) system and the feature needed to be determined; these techniques take a general CAD model, which is commercially available for providing an automated interface to extract and recognize the manufacturing features from the CAD model. Standard for Exchange of Product (STEP) format is an international standard for transporting geometric and non-geometric data between CAD and Computer Aided Manufacturing systems. Many researches have investigated the AFR in symmetrical features in rotational parts, but few studies have tried to recognize the cross-hole feature because of the recognition difficulty of such feature. A cross hole is recognized with existence of (B-spline Curve with Knots) string in (STEP AP203) neutral file. Cross holes might be perpendicular, offset or inclined to cylinder’s axis, it also can be blind or through. Extraction of the necessary information from (STEP) file for generating the parameters of the manufacturing operation becomes a significant task. In this paper, a new approach for automatic recognition of cross hole feature in hollow cylinders is developed, This approach has been built by connecting the Visual Basic programming language (VB) to the SolidWorks software. The drawing is sent to the VB after saving it as a (STEP AP203) file. VB extracts and recognizes the cross-hole feature according to the rule-based reasoning, some case studies for demonstrating the application of this method on different cases of the cross-hole are presented.

Phase selective protection in microgrids using combined data mining and modal decomposition method

This paper presents a combined data mining and modal decomposition method for phase selective protection in microgrids. A comprehensive fault analysis is performed in time domain to generate two feature sets as the most representative features by using modal decompositions of Clarke and symmetrical components transforms. The generated feature sets are then used in two procedures. In the first procedure, the Clark-based feature set is input into a single classifier to classify faults. In the second procedure, two series combined classifiers are used, in which the first classifier identifies the fault type using symmetrical components-based feature set and the second classifier determines the faulted phase(s) using the Clarke-based feature set. The proposed method is evaluated by extensive simulation of the standard IEC microgrid in islanded and grid connected modes, with radial or meshed topologies under different fault conditions. The simulation study validates the method, in particular it shows that the second procedure with the series combined classifiers provides significantly higher accuracy in fault type classification. The robustness of the proposed method is tested against measurement errors and external faults. The proposed method obviates the need for feature selection or additional processing steps and provides high accuracy at lower sampling frequency.

Symmetry-Protected Scattering in Non-Hermitian Linear SystemsSupported by the National Natural Science Foundation of China (Grant Nos. 11975128 and 11874225).

Symmetry plays fundamental role in physics and the nature of symmetry changes in non-Hermitian physics. Here the symmetry-protected scattering in non-Hermitian linear systems is investigated by employing the discrete symmetries that classify the random matrices. The even-parity symmetries impose strict constraints on the scattering coefficients: the time-reversal (C and K) symmetries protect the symmetric transmission or reflection; the pseudo-Hermiticity (Q symmetry) or the inversion (P) symmetry protects the symmetric transmission and reflection. For the inversion-combined time-reversal symmetries, the symmetric features on the transmission and reflection interchange. The odd-parity symmetries including the particle-hole symmetry, chiral symmetry, and sublattice symmetry cannot ensure the scattering to be symmetric. These guiding principles are valid for both Hermitian and non-Hermitian linear systems. Our findings provide fundamental insights into symmetry and scattering ranging from condensed matter physics to quantum physics and optics.

Research on Detection Method of Interest Flooding Attack in Named Data Networking

In order to effectively detect interest flooding attack (IFA) in Named Data Networking (NDN), this paper proposes a detection method of interest flooding attack based on chi-square test and similarity test. Firstly, it determines the detection window size based on the distribution of information name prefixes (that is information entropy) in the current network traffic. The attackers may append arbitrary random suffix to a certain prefix in the network traffic, and then send a large number of interest packets that cannot get the response. Targeted at this problem, the sensitivity of chi-square test is used to detect the change of prefix of interest packets. Interest packets initiated by IFA attackers are usually attached to a real prefix, but with a randomly generated suffix attached. Taking into account of this problem, the similarity of interest packet prefixes is further detected. Finally, the detection results of the two aspects are combined to determine whether interest flooding attack has occurred or not. In addition, according to the symmetric routing characteristic of Pending Interest Table (PIT), we also send the forged interest packet back to the attacker, and then restrict the corresponding port of the attacker, so as to effectively suppress the IFA attack. The experimental results show that the method we proposed can not only detect IFA in NDN at the beginning of the attack, but also is more accurate and effective than other methods.

Optimum Layouts of a Cluster of Heaving Point Absorbers in front of Wall-type Coastal Structures under Regular Wave Attack

The present paper aims at determining optimum layouts for a cluster of identical semi-immersed oblate spheroidal heaving Point Absorbers (PAs) in front of a bottom-mounted wall-type structure under the action of regular waves. Optimum layouts correspond to those that for a given incident wave frequency and direction maximize the power absorbed by the cluster and satisfy predefined spatial constraints. The corresponding optimization problem is solved by developing and coupling a Genetic Algorithm (GA) solver with a frequency-based hydrodynamic analysis numerical model. Initially, the efficiency of the developed optimization process is verified by comparing results with the parametric ones of other investigators. Next, various optimization cases for a cluster of five devices under the action of perpendicular to the wall waves are formed and solved. Focus is given on the effect of the incident wave frequency, the available for deploying the devices wall length and of symmetrical layout considerations, on the formation of the optimum layouts and on the power absorbed by the cluster. When optimization is performed at a frequency smaller than the heave natural frequency of the devices, the PAs within the optimum layouts are placed close to the wall and they form sub-clusters of closely-positioned bodies. Contrary to the above, maximization of the absorbed power at a frequency equal to the devices’ heave natural frequency is realized by placing the PAs at a large perpendicular distance from the wall and without forming any sub-clusters. In the latter case, the optimally-arranged devices show also a much better power absorption ability. The deployment of part of the whole wall length for placing the PAs reduces the power absorbed by the optimally-arranged clusters, compared to the cases, where no wall-length restrictions are taken into account. Finally, the consideration of symmetrical features in the formation of the optimum layouts affects at a small degree the power absorption ability of the PAs clusters.

Symmetrical feature extraction via novel Mirror PCA

Symmetry is an important property of human faces. Many researchers exploit symmetry to improve face recognition. In this paper, we explore the symmetry of faces under the framework of PCA (Principal Component Analysis). Unlike previous studies that manipulate facial symmetry by averaging the two halves, we argue that well-estimated symmetrical faces lie in a low-dimensional subspace. Inspired by this, a PCA-like model, referred to as Mirror PCA, is proposed to extract low-rank symmetrical features of faces, as well as their principal directions. Although the optimization problem is non-convex, a suboptimal solution could be found under existing optimization frameworks such as ADMM, PGD, etc. A number of experiments are conducted to verify the effectiveness and robustness of our method.

The Facial Aging Process From the "Inside Out".

The normal course of aging alters the harmonious, symmetrical, and balanced facial features found in youth, not only impacting physical attractiveness but also influencing self-esteem and causing miscommunication of affect based on facial miscues. With this evidence-based paper, the authors aim to provide a comprehensive overview of the latest research on the etiology and progression of facial aging by explaining the aging process from the “inside out,” that is, from the bony platform to the skin envelope. A general overview of the changes occurring within each of the main layers of the facial anatomy is presented, including facial skeleton remodeling, fat pad atrophy or repositioning, changes in muscle tone and thickness, and weakening and thinning of the skin. This is followed by an in-depth analysis of specific aging regions by facial thirds (upper, middle, and lower thirds). This review may help aesthetic physicians in the interpretation of the aging process and in prioritizing and rationalizing treatment decisions to establish harmonious facial balance in younger patients or to restore balance lost with age in older patients.

FONEMIK BAHASA DAYAK MALI

The Mali Dayak community resides in Balai-Batang Tarang Sub-district and a small part resides in Tayan Hilir District, Sanggau District. They have the same ethnic communication language, the Mali Dayak language. As of 2008 the number of Malian Dayak speakers was estimated at 6,963 people (Bamba, 2008). Languages with fewer than 10,000 speakers are classified as endangered (Crystal, 2000). If there is no attempt to maintain this Mali Dayak language, over time, this language is heading for extinction. Therefore, even the slightesteffort to preserve this language needs to be done. This study discusses the main problems regarding the Dayak Mali phonemic, namely the determination of phonemes, allophones, phoneme distribution, phoneme groups (cluster), phoneme sequences, and syllables of the Dayak Mali language which are based on structural theory. The results of this study indicate the phonemes of the Dayak Mali language, namely vowel, diphthong, consonant, and semivocal phonemes. Based on its distribution, a phonemic allophon is apparent. Thephonemic sequence also shows the environment of mutual influence and shows the symmetrical features of the phonemes found in the Dayak language of Mali. Similarly, the consonant group, the situation is answered through this research. The last problem examined is the syllables. The syllable structure contained in the Mali Dayak language is also described.

Application of Facial Symmetrical Characteristic to Transfer Learning

Most face detection and recognition tasks are based on the training of intact facial images and corresponding labels. Both the three‐dimensional (3D) structure and two‐dimensional (2D) appearance from the frontal view of human faces are bilaterally symmetrical in general. However, sometimes, illumination on the left and right halves of faces is uneven. In such cases, the symmetrical characteristic of human faces can facilitate expressing distinct identity information. This is because even if one side of the facial image is corrupted by noise, the opposite side can still be used for feature extraction. This paper proposes an automatic selection of the better half of the face using only a half‐face for identity recognition. Unlike the MegaFace challenge of recognizing millions of identities in the wild, this paper focuses on building recognition systems for a small number of people with fewer training images; the recognition system can, for example, build access control systems for research laboratory members or family members. This paper proposes an artificial face image construction method and a half‐face training strategy for transfer learning of pretrained conventional neural network models. Extensive experimental results show that the proposed method improves the performance of state‐of‐the‐art models by utilizing the symmetrical characteristics of human faces. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Vacant Parking Slot Recognition Method for Practical Autonomous Valet Parking System Using around View Image

The parking assist system (PAS) provides information of parking slots around the vehicle. As the demand for an autonomous system is increasing, intelligent PAS has been developed to park the vehicle without the driver’s intervention. To locate parking slots, most existing methods detect slot markings on the ground using an around-view monitoring (AVM) image. There are many types of parking slots of different shapes in the real world. Due to this fact, these methods either limit their target types or use predefined slot information of different types to cover the types. However, the approach using predefined slot information cannot handle more complex cases where the slot markings are connected to other line markings and the angle between slot marking is slightly different from the predefined settings. To overcome this problem, we propose a method to detect parking slots of various shapes without predefined type information. The proposed method is the first to introduce a free junction type feature to represent the structure of parking slot junction. Since the parking slot has a modular or repeated junction pattern at both sides, junction pair consisting of one parking slot can be detected using the free junction type feature. In this process, the geometrically symmetric characteristic of the junction pair is crucial to find each junction pair. The entrance of parking slot is reconstructed according to the structure of junction pair. Then, the vacancy of the parking slot is determined by a support vector machine. The Kalman tracker is applied for each detected parking slot to ensure stability of the detection in consecutive frames. We evaluate the performance of the proposed method by using manually collected datasets, captured in different parking environments. The experimental results show that the proposed method successfully detects various types of parking slots without predefined slot type information in different environments.

Localization for mixed far-field and near-field sources utilizing improved symmetric nested array

Nested arrays exhibit higher spatial resolution (SR) and enhanced degrees-of-freedom (DOFs) with fewer sensors, and they have been utilized for direction-of-arrival (DOA) estimation of both far-field (FF) and near-field (NF) sources. In this study, an improved symmetric nested array configuration with a given number of sensors was developed, which achieved increased consecutive and unique lags and thus resolved more targets than the actual number of inherent array sensors. In particular, the analytical expressions of the number of consecutive lags, the number of unique lags, and the virtual array aperture were derived for quantitative evaluation and comparison, as well as the corresponding array composition parameters of the optimal array geometry were obtained. In the mixed sources localization scheme, a special cumulant matrix was constructed to eliminate the range parameter in the NF steer vectors by exploiting the symmetric feature. Both subspace and sparse reconstruction techniques were exploited in order to directly obtain the DOAs of both FF and NF sources. With the estimated DOAs and the covariance matrix of the array output, the NF sources could be identified, and the corresponding range parameters could also be obtained by a one-dimension (1-D) spectrum search scheme. Numerous simulation results demonstrated that the proposed array showed a remarkable performance in terms of estimation accuracy, SR capacity, and numerous DOFs compared to state-of-the-art symmetric nested arrays.

EFS-LSTM (Ensemble-Based Feature Selection With LSTM) Classifier for Intrusion Detection System

In this article, an EFS-LSTM, a deep recurrent learning model, is proposed for network intrusion detection systems. The EFS-LSTM model uses ensemble-based feature selection (EFS) and LSTM (Long Short Term Memory) for the classification of network intrusions. The EFS combines five feature selection mechanisms namely, information gain, gain ratio, chi-square, correlation-based feature selection, and symmetric uncertainty-based feature selection. The experiments were conducted using the benchmark NSL-KDD dataset and implemented using Tensor flow and python. The EFS-LSTM classifier is evaluated using the classification performance metrics and also compared with all the 41 features without any feature selection as well as with each individual feature selection techniques and classified using LSTM. The performance study showed that the EFS-LSTM model outperforms better with 99.8% accuracy with a higher detection and less false alarm rates.

Hip-hop action image recognition based on symmetric algorithm and iterative weighting of dense sampling

With the rapid increase of hip-hop video data, facing a large number of rapidly updated data, automatic, accurate and fast analysis and recognition of relevant information in hip-hop video has become an urgent problem to be solved. An image recognition method of hip-hop action based on symmetric algorithm and iterative weighting of dense sampling is proposed. Firstly, the symmetric action feature of human hip-hop action image is extracted by the feature extraction method based on symmetric algorithm. Then, the symmetric action feature is obtained by fusion of the hip-hop action recognition algorithm based on iterative weighting of dense sampling. Finally, the action feature is obtained based on the fused action feature. The K-nearest neighbor classification learning method is used to recognize the hip-hop action image. The experimental results show that, under the same sample number, the number of omissions of children is 3, the extraction rate is 92.5%; the number of omissions of adults is 0, the extraction rate is 100%. This method can extract the human hip-hop image well, which lays a good foundation for the subsequent image recognition.

Mathematical Mirroring for Identification of Local Symmetry Centers in Microscopic Images Local Symmetry Detection in FIJI.

Symmetry is omnipresent in nature and we encounter symmetry routinely in our everyday life. It is also common on the microscopic level, where symmetry is often key to the proper function of core biological processes. The human brain is exquisitely well suited to recognize such symmetrical features with ease. In contrast, computational recognition of such patterns in images is still surprisingly challenging. In this paper we describe a mathematical approach to identifying smaller local symmetrical structures within larger images. Our algorithm attributes a local symmetry score to each image pixel, which subsequently allows the identification of the symmetrical centers of an object. Though there are already many methods available to detect symmetry in images, to the best of our knowledge, our algorithm is the first that is easily applicable in ImageJ/FIJI. We have created an interactive plugin in FIJI that allows the detection and thresholding of local symmetry values. The plugin combines the different reflection symmetry axis of a square to get a good coverage of reflection symmetry in all directions. To demonstrate the plugins potential, we analyzed images of bacterial chemoreceptor arrays and intracellular vesicle trafficking events, which are two prominent examples of biological systems with symmetrical patterns.

A Front Water Recognition Method Based on Image Data for Off-Road Intelligent Vehicle

Off-road intelligent vehicle is an important application about Internet of Vehicles technology used in the transportation field, and the front obstacle recognition method is the key technology for off-road intelligent vehicle. In this paper, based on smart data aggregation inspired paradigm of IoT applications, we mainly study perception technology in vehicle networking by using image data and one symmetrical speeded-up robust features detector (SURF). By considering symmetry and image data aggregation, we found that data aggregation had the ability of providing global information for Internet of Vehicles systems. After we have built the experiment platform, the experiment results showed that this method is faster than Scale-Invariant Feature Transform algorithm in this case, which can satisfy the water detection accuracy and the real-time requirement. So, this method is effective for the water images detection with great symmetry to off-road intelligent vehicle, and it also gives a useful reference about environment perception technology and smart data aggregation inspired paradigm used in future Internet of Vehicles, intelligent vehicle, and traffic safety applications.

High-Gain Circular Patch Antenna and Array With Introduction of Multiple Shorting Pins

This article proposes gain-enhanced circular patch antenna and array with introduction of multiple shorting pins. The antenna consists of a circular patch and several shorting pins. Equivalent inductance is brought to the patch after introducing shorting pins. Thus, resonance of the patch shifts at higher frequency. Compared to a conventional circular patch without any pins, radiation area of the proposed patch is enlarged given the same resonance frequency, hence leads to gain enhancement. The number of pins introduced to a circular patch can be set freely due to its symmetrical feature. Moreover, surface current is restricted within a small area around the center of the patch after introducing multiple pins, leading to more concentrated radiation. An equivalent circuit model is built for the proposed antenna. Layout and number optimization rules for introducing shorting pins are formulated. With these rules, gain of a circular patch can be maximized for the applications under certain size restrictions. A prototype operating at 5.8 GHz is designed and fabricated. Furthermore, a 2*2 array based on the gain-enhanced circular patch elements is implemented. In the measurements, high gain, high efficiency, and narrowed half-power beamwidth of the patch antenna and the array are observed.

Symmetric–asymmetric hybrid encryption and decryption system based on chaotic iris phase mask and computer-generated holography

Based on chaotic biological cryptography and computer-generated holography, a symmetric–asymmetric hybrid encryption and decryption system was proposed. This method has multiple keys, in which two decryption keys are different from the encryption keys. The encryption and decryption system possesses both symmetric and asymmetric key cryptography features. The biometric code is unique and unchangeable, not easy to be stolen or lost, which further enhances the security of the system. Chaos technology can reduce the amount of information, when combined with the advantages of the digitization of computer-generated holography, which can be conducive to the preservation and the transmission of encrypted information. The numerical simulation results show that this scheme is feasible and has better security and higher robustness.

Novel multi-label feature selection via label symmetric uncertainty correlation learning and feature redundancy evaluation

Multi-label data with high dimensionality, widely existed in the real world, bring many challenges to the applications of machine learning, pattern recognition and other fields. Scholars have proposed some multi-label feature selection methods from various aspects. However, there are few studies on the feature selection for multi-label data based on fuzzy mutual information, and most existing methods neglect the correlation between labels. In this study, we propose two novel multi-label feature selection approaches via label symmetric uncertainty correlation and feature redundancy evaluation. Firstly, we propose the concept of symmetric uncertainty correlation between labels via fuzzy mutual information, and design a label importance weight based on label symmetric uncertainty correlation learning. Further, we define a label similarity relation matrix on multi-label space via the label importance weight. Secondly, we define the symmetric uncertainty correlation between features and labels, and propose the first multi-label feature selection approach. Thirdly, considering the above-proposed method can only get a feature sequence and does not remove the redundancy features, we further propose an improved multi-label removing-redundancy feature selection approach through introducing feature redundancy evaluation. Finally, comprehensive experiments are executed to demonstrate the performance of our methods. The results illustrate that our study is better than other representative feature selection methods.

The heritage of the twin microstructure in the sigma phase formed from deformed austenite

Deformation twinning is the main mechanism for the nanocrystallization of austenite and the sigma phase, which has been found to nucleate at deformation twin boundaries, especially at their intersections in over-deformed austenite stainless steel. In the present paper, deformation twin heritage is found in sigma nuclei in the early stage of sigma phase transformation, due to the continuous feature of twin boundaries. The sigma nuclei change their crystal orientation by following the symmetrical feature of deformation twin boundaries during growth. This unique nucleation and growth pattern of the sigma phase is in favour of decreasing the energy required for sigma transformation from deformed austenite.