Pengelolaan Program Keahlian Teknik Pemesinan (Studi Situs di SMK Binawiyata Sragen)

Tujuan penelitian ini adalah (1) Mendeskripsikan karakteristik pengelolaan kurikulum program keahlian teknik pemesinan di SMK Binawiyata Kabupaten Sragen. (2) Mendeskripsikan karakteristik kompetensi guru program keahlian teknik pemesinan di SMK Binawiyata Kabupaten Sragen, dan (3) Mendeskripsikan karakteristik tata letak ruang pembelajaran program keahlian teknik pemesinan di SMK Binawiyata Kabupaten Sragen. Jenis penelitian ini menggunakan bentuk penelitian kualitatif dan dengan menggunakan desain penelitian etnografi. Lokasi penelitian ini adalah di SMK Binawiyata Sragen. Teknik pengumpulan data dilakukan dengan observasi, wawancara mendalam, dan dokumentasi. Model analisis data dalam penelitian ini menggunakan metode analisis data tertata dalam situs untuk diskripsi. Uji keabsahan data atau memeriksa kebenaran data digunakan cara pengamatan yang terus-menerus, trianggulasi, dan membicarakan dengan orang lain atau rekan sejawat. Hasil penelitian: (1) Tujuan kurikulum program keahlian teknik pemesinan adalah membekali peserta didik dengan keterampilan, pengetahuan dan sikap agar dapat bekerja baik secara mandiri atau mengisi lowongan pekerjaan yang ada didunia usaha dan dunia industri sebagai tenaga kerja tingkat menengah dalam bidang Teknik Pemesinan atau memilih karir, berkompetisi, dan mengembangkan sikap profesional dalam bidang teknik pemesinan, dengan kompetensi: (a) Menjelaskan: dasar kekuatan bahan & komponen mesin, prinsip dasar kelistrikan & konversi energi, proses dasar perlakuan logam, (b) Menggunakan: perkakas tangan, perkakas bertenaga/operasi digenggam, peralatan pembanding dan/atau alat ukur dasar, (c) Melakukan pekerjaan dengan mesin frais, bubut, gerinda dan mesin bubut (kompleks), (d) Mengeset, memprogram & mengoperasikan mesin CNC (dasar). (2) Guru pada SMK Binawiyata Kabupaten Sragen Program Keahlian Teknik Pemesinan berjumlah 30 guru termasuk guru agama dan olah raga, kesemuanya telah memiliki kualifikasi akademik pendidikan minimum diploma empat atau sarjana yang sesuai dengan mata pelajaran yang diajarkan/diampu. Kompetensi guru meliputi: kompetensi pedagogik, kompetensi sosial, dan kompetensi profesional. (3) Ruang pembelajaran program keahlian teknik pemesinan terdiri dari ruang teori dan ruang praktik, ditata sesuai dengan fungsi ruang masing-masing. Ruang praktik ditata agar berfungsi sebagai tempat berlangsungnya kegiatan pembelajaran: pekerjaan logam dasar, pengukuran dan pengujian logam, membubut lurus, bertingkat, tirus, ulir luar dan dalam, memfrais lurus, bertingkat, roda gigi, menggerinda-alat, dan pengepasan/pemasangan komponen. Penataan ruang praktik diletakkan terpisah dengan ruang teori

Image resolution enhancement using dual-tree complex wavelet transform

In this letter, a complex wavelet-domain image resolution enhancement algorithm based on the estimation of wavelet coefficients is proposed. The method uses a forward and inverse dual-tree complex wavelet transform (DT-CWT) to construct a high-resolution (HR) image from the given low-resolution (LR) image. The HR image is reconstructed from the LR image, together with a set of wavelet coefficients, using the inverse DT-CWT. The set of wavelet coefficients is estimated from the DT-CWT decomposition of the rough estimation of the HR image. Results are presented and discussed on very HR QuickBird data, through comparisons between state-of-the-art resolution enhancement methods

Gait recognition and understanding based on hierarchical temporal memory using 3D gait semantic folding

Gait recognition and understanding systems have shown a wide-ranging application prospect. However, their use of unstructured data from image and video has affected their performance, e.g., they are easily influenced by multi-views, occlusion, clothes, and object carrying conditions. This paper addresses these problems using a realistic 3-dimensional (3D) human structural data and sequential pattern learning framework with top-down attention modulating mechanism based on Hierarchical Temporal Memory (HTM). First, an accurate 2-dimensional (2D) to 3D human body pose and shape semantic parameters estimation method is proposed, which exploits the advantages of an instance-level body parsing model and a virtual dressing method. Second, by using gait semantic folding, the estimated body parameters are encoded using a sparse 2D matrix to construct the structural gait semantic image. In order to achieve time-based gait recognition, an HTM Network is constructed to obtain the sequence-level gait sparse distribution representations (SL-GSDRs). A top-down attention mechanism is introduced to deal with various conditions including multi-views by refining the SL-GSDRs, according to prior knowledge. The proposed gait learning model not only aids gait recognition tasks to overcome the difficulties in real application scenarios but also provides the structured gait semantic images for visual cognition. Experimental analyses on CMU MoBo, CASIA B, TUM-IITKGP, and KY4D datasets show a significant performance gain in terms of accuracy and robustness

Clique descriptor of affine invariant regions for robust wide baseline image matching

Assuming that the image distortion between corresponding regions of a stereo pair of images with wide baseline can be approximated as an affine transformation if the regions are reasonably small, recent image matching algorithms have focused on affine invariant region (IR) detection and its description to increase the robustness in matching. However, the distinctiveness of an intensity-based region descriptor tends to deteriorate when an image includes homogeneous texture or repetitive pattern. To address this problem, we investigated the geometry of a local IR cluster (also called a clique) and propose a new clique-based image matching method. In the proposed method, the clique of an IR is estimated by Delaunay triangulation in a local affine frame and the Hausdorff distance is adopted for matching an inexact number of multiple descriptor vectors. We also introduce two adaptively weighted clique distances, where the neighbour distance in a clique is appropriately weighted according to characteristics of the local feature distribution. Experimental results show the clique-based matching method produces more tentative correspondences than variants of the SIFT-based method

Multi-set canonical correlation analysis for 3D abnormal gait behaviour recognition based on virtual sample generation

Small sample dataset and two-dimensional (2D) approach are challenges to vision-based abnormal gait behaviour recognition (AGBR). The lack of three-dimensional (3D) structure of the human body causes 2D based methods to be limited in abnormal gait virtual sample generation (VSG). In this paper, 3D AGBR based on VSG and multi-set canonical correlation analysis (3D-AGRBMCCA) is proposed. First, the unstructured point cloud data of gait are obtained by using a structured light sensor. A 3D parametric body model is then deformed to fit the point cloud data, both in shape and posture. The features of point cloud data are then converted to a high-level structured representation of the body. The parametric body model is used for VSG based on the estimated body pose and shape data. Symmetry virtual samples, pose-perturbation virtual samples and various body-shape virtual samples with multi-views are generated to extend the training samples. The spatial-temporal features of the abnormal gait behaviour from different views, body pose and shape parameters are then extracted by convolutional neural network based Long Short-Term Memory model network. These are projected onto a uniform pattern space using deep learning based multi-set canonical correlation analysis. Experiments on four publicly available datasets show the proposed system performs well under various conditions

Silhouette-based gait recognition using Procrustes shape analysis and elliptic Fourier descriptors

This paper presents a gait recognition method which combines spatio-temporal motion characteristics, statistical and physical parameters (referred to as STM-SPP) of a human subject for its classification by analysing shape of the subject's silhouette contours using Procrustes shape analysis (PSA) and elliptic Fourier descriptors (EFDs). STM-SPP uses spatio-temporal gait characteristics and physical parameters of human body to resolve similar dissimilarity scores between probe and gallery sequences obtained by PSA. A part-based shape analysis using EFDs is also introduced to achieve robustness against carrying conditions. The classification results by PSA and EFDs are combined, resolving tie in ranking using contour matching based on Hu moments. Experimental results show STM-SPP outperforms several silhouette-based gait recognition methods

Great Expectations: Incest and Incompleteness in Kathy Acker’s Blood and Guts in High School

Often situated as a radical response to the late 1970s New York punk scene, the work of American writer Kathy Acker leverages an array of subversive literary techniques to actively interrogate extremely uncomfortable social terrain: profound violence against women, physical and emotional abuse, incest, disease and severe neglect. Many of her protagonists navigate through a continual proliferation of atrocities. Yet rather than situate her characters as victims, Acker instead inverts prescribed social scripts and proactively constructs narrative webs of deeply embedded critiques of patriarchal and sexual oppression. By deploying a vast repertoire of forms – theatrical dialogues, drawings, dream maps, blatant plagiarism of canonical figures (e.g., Hawthorne, Mallarmé, Céline), fake translations – Acker paints a vivid and inventive picture of the apparatuses of control and manipulation, aggression and alienation. This essay seeks to examine how applications of logician Kurt Gödel’s incompleteness theorem and cultural critic Nick Mansfield’s ideas about “masochism as a theatrical space of power” elucidate Acker’s watershed novel Blood and Guts in High School and examine the novel’s critique of social and sexual power.Udostępnienie publikacji Wydawnictwa Uniwersytetu Łódzkiego finansowane w ramach projektu „Doskonałość naukowa kluczem do doskonałości kształcenia”. Projekt realizowany jest ze środków Europejskiego Funduszu Społecznego w ramach Programu Operacyjnego Wiedza Edukacja Rozwój; nr umowy: POWER.03.05.00-00-Z092/17-00

Eksperimentasi Pembelajaran Matematka Menggunakan Model Pembelajaran Think Pair Share Dengan Pendekatan Pembelajaran Matematika Realistik Pada Pokok Bahasan Dimensi Tiga Ditinjau Dari Kecerdasan Spasial Siswa

This research was aimed at searching and finding: 1) the most effective mathematics learning model among TPS learning model with PMR, TPS learning model, and direct learning model, 2) the level of student\u27s spatial intelligence having the highest achievement among students with high, average, and low spatial intelligence, 3) the most effective learning model among TPS learning model with PMR, TPS learning model, and direct learning model towards student\u27s achievement on each level of spatial intelligence, and 4) the level of students having the highest achievement in every learning model. This was a quasy-experimental research with a 3x3 factorial design. The population was students of grade X of state senior high school in Surakarta in 2013/2014. Stratified random sampling and cluster random sampling techniques were applied. The samples in this research were: 1) experiment group 1, consisting of 91 students; 2) experiment group 2, consisting of 90 students; 3) control group, consisting of 99. The data collecting instruments were student\u27s spatial intelligence test and achievement test in the form of multiple choices. Balance test with unbalanced one-way anova test, analysis prerequisite tests (normality test with Liliefors test and homogenity test with Bartlett test) and hipothesis test (unbalanced two-way anova test) were conducted. It can be concluded that: 1) TPS model with PMR is more effective towards student\u27s achievement than TPS model and direct model, and TPS model is as effective as direct model towards student\u27s achievement; 2) students with high spatial intelligence gain higher achievement than those with average and low spatial intelligence, and students with average spatial intelligence gain higher achievement than those with low spatial intelligence; 3) to students with high and average spatial intelligence, TPS model with PMR, TPS model and direct model give the same achievement. For students with low spatial intelligence, TPS model with PMR and TPS model give the same achievement, but TPS model with PMR gain higher achievement than those with direct model, and TPS model and direct model give the same achievement; and 4) dealing with TPS model with PMR, students with high and average spatial intelligence gain the same of achievement, but students with high spatial intelligence gain higher achievement than those with low spatial intelligence, and students with average and low spatial intelligence gain the same of achievement, while dealing with with TPS model, students with high and average spatial intelligence gain the same of achievement but students with high spatial intelligence gain higher than those with low spatial intelligence, while students with average spatial intelligence gain higher achievement than those with low spatial intelligence, and dealing with direct model, students with high spatial intelligence gain higher achievement than those with average and low spatial intelligence, but students with average and low spatial intelligence gain the same of achievement

2.5D multi-view gait recognition based on point cloud registration

This paper presents a method for modeling a 2.5-dimensional (2.5D) human body and extracting the gait features for identifying the human subject. To achieve view-invariant gait recognition, a multi-view synthesizing method based on point cloud registration (MVSM) to generate multi-view training galleries is proposed. The concept of a density and curvature-based Color Gait Curvature Image is introduced to map 2.5D data onto a 2D space to enable data dimension reduction by discrete cosine transform and 2D principle component analysis. Gait recognition is achieved via a 2.5D view-invariant gait recognition method based on point cloud registration. Experimental results on the in-house database captured by a Microsoft Kinect camera show a significant performance gain when using MVSM